The URL can be used to link to this page

Home My WebLink AboutMemo - Read Before Packet - 7/23/2019 - Memorandum From Noah Beals And Rebecca Everette Re: Work Session Item #1 Staff Report: Digital BillboardsAttachment 1 Attachment 2 Attachment 3 Agenda Item 20 Item # 20 Page 1 AGENDA ITEM SUMMARY March 19, 2019 City Council STAFF Noah Beals, Senior City Planner/Zoning Chris Van Hall, Legal SUBJECT Resolution 2019-037 Setting a Time Frame and Giving Policy Direction Regarding the Consideration of Digital Billboard Regulations. EXECUTIVE SUMMARY The purpose of this item is to establish a time frame for City Council to hear an ordinance regarding Digital Billboard Regulations. STAFF RECOMMENDATION Staff recommends the adoption of Resolution. BACKGROUND / DISCUSSION At the February 26, 2019 Work Session, City Council discussed Digital Billboard Regulations. Council directed additional public outreach be conducted and requested more information. Additionally, it was requested that a resolution be adopted providing a time frame for an ordinance to be heard with key components of the ordinance to include a replacement ratio and an option for Council to have an alternative review for a digital billboard application. ATTACHMENTS 1. Work Session Summary, February 26, 2019 (PDF) 2. Powerpoint presentation (PDF) ATTACHMENT 1 March 19th , 2019 Digital Billboard Resolution Noah Beals Senior City Planner - Zoning ATTACHMENT 2 2 • The purpose of this resolution is to provide a time frame and policy directives for an ordinance on digital billboards regulations. Phase 2 Sign Code Update 3 1. Phase 2 Sign Code Update was approved in December of 2018. 2. Digital Billboard Regulations were separated from the sign code update. Timeframe 4 • An ordinance to be brought forward by September 3rd, 2019. • Additional public outreach to discuss: • Replacement Ratio • Number of locations • Size of digital billboards Policy Directives 5 • Digital Billboard Regulations to include a replacement ratio. • A Digital Billboard Application to be reviewed by the Planning and Zoning Board. • City Council can conduct a rehearing of a Digital Billboard Application. 6 • The purpose of this resolution is to provide timeframe and policy directives for an ordinance on digital billboards regulations. -1- RESOLUTION 2019-037 OF THE COUNCIL OF THE CITY OF FORT COLLINS SETTING A TIME FRAME AND GIVING POLICY DIRECTION REGARDING THE CONSIDERATION OF DIGITAL BILLBOARD REGULATIONS WHEREAS, on December 2, 1997, by its adoption of Ordinance No. 190, 1997, the City Council enacted the Fort Collins Land Use Code; and WHEREAS, the Land Use Code contains regulations regarding signs within the City; and WHEREAS, on December 4, 2018, the City adopted new sign regulations with regards to the issues of content and viewpoint neutrality; and WHEREAS, as part of these new sign regulations, Council has also been considering adopting new regulations related to digital billboards within the City; and WHEREAS, at a February 26, 2019, work session, Council asked for additional public outreach and more information related to digital billboard regulations; and WHEREAS, the purpose and intent of this Resolution is to set a time frame for adoption of regulations related to digital billboards and to give City staff additional policy directives to include in the proposed digital billboard regulations, including a program to reduce eight existing non-digital billboards within the City and the Growth Management Area (“GMA”) in exchange for one digital billboard within the City and that review of any digital application will be subject to Type 2 review under the Land Use Code (“LUC”), subject to appeal in accordance with Chapter 2, Article II, Division 3 of the City Code; and WHEREAS, the City Council has determined that the continued regulation of digital signs will promote Council’s objectives and public purposes, protect the health, safety and welfare of City residents and is in the best interests of the City and its citizens. NOW, THEREFORE, BE IT ORDAINED BY THE COUNCIL OF THE CITY OF FORT COLLINS as follows: Section 1. That the City Council hereby makes and adopts the determinations and findings contained in the recitals set forth above. Section 2. That staff is directed to bring forward digital billboard regulations at a regularly scheduled Council meeting on or before September 3, 2019, after completing additional public outreach. Section 3. That the proposed digital billboard regulations will include a program to allow for the construction and maintenance of one digital billboard on property within the City that meets standards identified in the proposed regulations in exchange for the removal of eight existing non-digital billboards within the City and GMA. -2- Section 4. That the proposed digital billboard regulations will require digital billboard applications to be subject to a Type 2 review under the LUC, subject to appeal in accordance with Chapter 2, Article II, Division 3 of the City Code. Section 5. That this Resolution is meant to provide policy direction regarding the proposed digital billboard regulations and Council reserves the authority and right to make any additional changes to the proposed regulations, whether consistent or inconsistent with this Resolution, at its discretion. Passed and adopted at a regular meeting of the Council of the City of Fort Collins this 19th day of March, A.D. 2019. _________________________________ Mayor ATTEST: _____________________________ City Clerk Attachment 4 Attachment 5 DATE: STAFF: February 26, 2019 Noah Beals, Senior City Planner/Zoning WORK SESSION ITEM City Council SUBJECT FOR DISCUSSION Digital Billboard Regulations. EXECUTIVE SUMMARY The purpose of this item is to further discuss the replacement of static billboards with digital billboards. GENERAL DIRECTION SOUGHT AND SPECIFIC QUESTIONS TO BE ANSWERED Does Council want to proceed with consideration of an ordinance introducing digital billboard regulations? BACKGROUND / DISCUSSION Council requested additional information concerning the proposed digital billboard standards. These requests, which are listed below, are from the August 14, 2018 work session where staff presented the draft of the proposed Sign Code update that included a section containing an option to convert existing static billboards in both the City and Growth Management Area to a limited number of digital billboard locations. Council requests: • Council requested exact information on the number, location and design details of the existing static billboards. Attachment 1 is a map that illustrates the existing billboards. The total number of existing billboards is 144, within 85 different locations. Of the 144 billboards, 35% are greater than 90 square feet in size. • Council requested data on safety issues related to digital billboards and the proposed standards. LAMAR advertising company provided an email to Council on September 7, 2018 that included a report on digital billboards and an article on safety studies. The report (Attachment 2) was commissioned by the Federal Highway Administration and presented three conclusions. o Conclusion: Commercial electronic variable message signs (CEVMS) do not appear to be related to a decrease in looking toward the road ahead. o Conclusion: Driver view time of a CEVMS did not increase compared to view time of a static billboard and the view time is found to be within the acceptable threshold of National Highway Traffic Safety Administration (NHTSA). o Conclusion: The study added to the knowledge base on the issues examined but did not present definitive answers to the research question investigated. In a publication of the American Planning Association, Zoning Practice Smart Sign Codes the finding of several studies was reported as follows: o 2006 Study by the National Highway Traffic Safety Administration focused on driver distractions and found any distraction of more than 2 seconds is a potential cause of crashes and near crashes. o 2004 study by the University of Toronto found that drivers make twice as many glances at video signs than they do at static signs and drivers’ glances at an active sign were longer in duration. o Prior to 2004, the University of Toronto found that drivers made the same number of glances at traffic signals and street signs with and without video billboards present. February 26, 2019 Page 2 o 2005 study by Texas transportation found that flashing and changing messages are more distracting and require more reading time to comprehend the message. o 2001 study commissioned by the City of Seattle concluded that electronic signs that moving/flash images distract drivers for longer intervals than electronic signs with no movement. This report recommended a 10 second message display time. With different reports and findings, the data does not reach a conclusive result. • Council requested examples of other jurisdictions that proposed similar approach to reducing billboards. The following are other jurisdictions conversion rates: o San Diego County considered two options: ▪ 3:1 ratio: remove 3 static billboards and place 1 electronic billboard. ▪ 3:1 ratio in square footage, remove 600 sf of static billboards for 200 sf of electronic billboard. After review and consideration, San Diego County choose to not allow digital billboards. o Nevada Department of Transportation does not have a ratio. They allow the conversion to take place if it meets all other state and local standards. o St. Petersburg, Florida agreed with a billboard company to remove 83 static billboards for the 6 new digital billboards (14:1 ratio). o Kalamazoo, Michigan is currently drafting an ordinance with a 6:1, ratio with a maximum of 8 digital billboards. o Minnetonka, MI agreed with a billboard company to the removal of 15 out of 30 static billboards in exchange for 8 digital billboards (almost a 2:1 ratio). o San Antonio, TX ordinance requires the removal of 4 static billboards in exchange for 1 digital billboard. • Council requested the analysis on the proposed 8:1 ratio recommended by staff and the consultant: In proposing a ratio for a billboard exchange, the goal is to reduce the number of existing billboards as much as possible. The proposed 8:1 ratio affects the majority of existing 85 billboard locations. The first approach suggested a setback distance for a digital billboard from any other billboard (existing or new). The thought was the setback would eliminate most existing billboards; however, staff found the following: o 10 digital billboards with 2 mile spacing, removes 71 sign structures (2 mile radius): 6 posters, 21 junior posters, 44 bulletins. o 8 digital billboards with 3 mile spacing, removes 75 sign structures (2 mile radius): 6 posters, 21 junior posters, 48 bulletins. o 6 digital billboards with 4 mile spacing, removes 51 signs (2 mile radius). Additionally, this approach assumed a new digital billboard could be in the same area of most of the existing billboards. This assumption is not accurate based on state and federal restrictions along Mulberry and parts of I-25. The conclusion was made that a 2-mile separation from another billboard with the required number and square footage reduction would provide greater flexibility for the applicant in deciding which to remove while ensuring the removal of existing static billboards. • Council requested the analysis on the proposed message display time recommended by staff and the consultant: Staff looked at the display times of other jurisdictions and found the following: City Message change delay time City Message change delay time Greeley 30 seconds Thornton 5 seconds February 26, 2019 Page 3 Loveland 5 seconds Longmont 60 seconds Lakewood 8 seconds Provo 3 times a day/ high churn 8 seconds Boulder 60 seconds Gainesville electronic signs prohibited Westminster 30 minutes Denton None Arvada 8 seconds Burbank electronic signs prohibited Fort Collins Current 60 seconds Fort Collins proposed 24 seconds along roads with speed limits of 50MPH and 60 seconds along all other roads While most communities allow a display time of less than 60 seconds, a 60 second display time is not unprecedented. The intent of a longer display time addresses both the safety and aesthetics of digital signs. The Land Use Code prohibits flashing/blinking signs, and the goal is to strike a balance between the aesthetics of a flashing/blinking sign and a static sign. Colorado Department of Transportation requires a four second dwell time. Typical add space is sold in 8 to 10 second intervals. Working with the 8 second interval, 16 seconds provided a greater number of changes increasing the driver distraction. A 24 second display time provides more of a static appearance along the I- 25 and addresses both safety and aesthetics of signs. While the 24 second display time is less than 60 seconds, reducing the delay time would be acceptable because at higher speeds the time a sign is viewed is reduced. Again, it is a balance between creating an appearance of a flashing/blinking sign and a static sign. • Council requested the digital billboard standards be presented to the Natural Resources Advisory Board (NRAB). On September 19, 2018 the NRAB voted in support of the proposed digital billboard regulations. Additionally, the Board provided the following points: o As static signs are being considered for replacement, those with existing illumination should be of the highest priority for removal. o We would like to see language within the code that makes it illegal to have signs face into any natural area. o We would discourage the placement of digital signs in our gateway areas (Harmony Road, Prospect Road, and Mulberry Road) One of the ideas brought up by NRAB that has not been discussed yet is the idea of incentivizing the removal of static billboards that already are luminated. Staff plan to further explore this idea. February 26, 2019 Page 4 In addition to presenting to the NRAB staff we discussed the proposed digital billboard standards with the City’s internal Night Sky team. This team discussed the following topics: o Brightness levels between sunset and sunrise o Color temperature at night (blue + white spectrum light) o Dwell times for changing messages o Exchange of static billboards for Digital Billboards o Safety (distraction + glare) These discussions focused on safety for both human and animals. Additionally, studies on the effects of lighting at night were provided (Attachment 6). Overall, the preference is for no artificial light. However, if artificial light is necessary, than it was suggested that it be adjustable to blend with the ambient light levels. • Council requested information on the Return on Investment (ROI) for an applicant pursing a new digital billboard. The City requested such information from LAMAR, the owner of the majority of billboards in Fort Collins. The response from LAMAR discussed previous ratio replacements they conducted in other jurisdictions that have all been 3:1 or less. LAMAR indicated these have worked well and concluded that a 4:1 ratio would work for their goals. However, they do not see how an 8:1 ratio would be economically viable. Additionally, staff asked if a 6:1 ratio would work, and while they are appreciative of a ratio less than 8:1, they cannot guarantee that any more than a 4:1 ratio is viable. No empirical data was provided. Conclusion Would Council like to proceed with consideration of an ordinance to introduce digital billboard regulations? ATTACHMENTS 1. Existing Billboard Location Map (PDF) 2. Digital Billboard and Driver Visual Behavior Peer Reviewed Report (PDF) 3. Digital Billboards Agents of Safety (PDF) 4. Smart Sign Codes Zoning Practice (PDF) 5. National Resource Advisory Board Memo on digital sign change (PDF) 6. Health Effects of Light Pollution (PDF) 7. Ecological Light Pollution (PDF) 8. Digital Billboard Regulations (Draft) (PDF) 9. Powerpoint Presentation (PDF) INTERSTATE 25 S SHIELDS ST S COLLEGE AVE S TAFT HILL RD E VINE DR S TIMBERLINE RD LAPORTE AVE S LEMAY AVE E PROSPECT RD E DOUGLAS RD E TRILBY RD W DRAKE RD E HARMONY RD N OVERLAND TRL E DRAKE RD W TRILBY RD W PROSPECT RD N SHIELDS ST E MULBERRY ST COUNTY ROAD 54G STATE HIGHWAY 392 W MULBERRY ST SE FRONTAGE RD S OVERLAND TRL SW FRONTAGE RD E COUNTY ROAD 30 S COUNTY ROAD 5 STOVER ST W OAK ST NE FRONTAGE RD E LINCOLN AVE RIVERSIDE AVE CARPENTER RD W COUNTY ROAD 38E W ELIZABETH ST S COUNTY ROAD 23 BAY RD RICHARDS LAKE RD N US HIGHWAY 287 N TAFT HILL RD W HORSETOOTH RD W HARMONY RD TURNBERRY RD N COLLEGE AVE MAIN ST WHEDBEE ST E STUART ST COUNTRY CLUB RD HIDDEN SPRINGS RD N LEMAY AVE BIGHORN XING W SWALLOW RD SMITH ST S COUNTY ROAD 19 W MOUNTAIN AVE S MASON ST TERRY LAKE RD PETERSON ST SENECA ST MAX GUIDEWAY DRIVER VISUAL BEHAVIOR IN THE PRESENCE OF COMMERCIAL ELECTRONIC VARIABLE MESSAGE SIGNS (CEVMS) SEPTEMBER 2012 FHWA-HEP- ATTACHMENT 2 FOREWORD The advent of electronic billboard technologies, in particular the digital Light-Emitting Diode (LED) billboard, has necessitated a reevaluation of current legislation and regulation for controlling outdoor advertising. In this case, one of the concerns is possible driver distraction. In the context of the present report, outdoor advertising signs employing this new advertising technology are referred to as Commercial Electronic Variable Message Signs (CEVMS). They are also commonly referred to as Digital Billboards and Electronic Billboards. The present report documents the results of a study conducted to investigate the effects of CEVMS used for outdoor advertising on driver visual behavior in a roadway driving environment. The report consists of a brief review of the relevant published literature related to billboards and visual distraction, the rationale for the Federal Highway Administration research study, the methods by which the study was conducted, and the results of the study, which used an eye tracking system to measure driver glances while driving on roadways in the presence of CEVMS, standard billboards, and other roadside elements. The report should be of interest to highway engineers, traffic engineers, highway safety specialists, the outdoor advertising industry, environmental advocates, Federal policymakers, and State and local regulators of outdoor advertising. Monique R. Evans Director, Office of Safety Research and Development Nelson Castellanos Director, Office of Real Estate Services Notice This document is disseminated under the sponsorship of the U.S. Department of Transportation in the interest of information exchange. The U.S. Government assumes no liability for the use of the information contained in this document. This report does not constitute a standard, specification, or regulation. The U.S. Government does not endorse products or manufacturers. Trademarks or manufacturers’ names appear in this report only because they are considered essential to the objective of the document. Quality Assurance Statement The Federal Highway Administration (FHWA) provides high-quality information to serve government, industry, and the public in a manner that promotes public understanding. Standards and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its information. The FHWA periodically reviews quality issues and adjusts its programs and processes to ensure continuous quality improvement. TECHNICAL DOCUMENTATION PAGE 1. Report No. FHWA-HRT- 2. Government Accession No. 3. Recipient’s Catalog No. 4. Title and Subtitle Driver Visual Behavior in the Presence of Commercial Electronic Variable Message Signs (CEVMS) 5. Report Date 6. Performing Organization Code 7. Author(s) William A. Perez, Mary Anne Bertola, Jason F. Kennedy, and John A. Molino 8. Performing Organization Report No. 9. Performing Organization Name and Address SAIC 6300 Georgetown Pike McLean, VA 22101 10. Work Unit No. (TRAIS) 11. Contract or Grant No. 12. Sponsoring Agency Name and Address Office of Real Estate Services Federal Highway Administration 1200 New Jersey Avenue SE Washington, DC 20590 13. Type of Report and Period Covered 14. Sponsoring Agency Code 15. Supplementary Notes The Contracting Officer’s Technical Representatives (COTR) were Christopher Monk and Thomas Granda. 16. Abstract This study was conducted to investigate the effect of CEVMS on driver visual behavior in a roadway driving environment. An instrumented vehicle with an eye tracking system was used. Roads containing CEVMS, standard billboards, and control areas with no off-premise advertising were selected. Data were collected on arterials and freeways in the day and nighttime. Field studies were conducted in two cities where the same methodology was used but there were differences in the roadway visual environment. The gazes to the road ahead were high across the conditions; however, the CEVMS and billboard conditions resulted in a lower probability of gazes as compared to the control conditions (roadways not containing off-premise advertising) with the exception of arterials in Richmond where none of the conditions differed from each other. Examination of where drivers gazed in the CEVMS and standard billboard conditions showed that gazes away from the road ahead were not primarily to the billboards. Average and maximum fixations to CEVMS and standard billboards were similar across all conditions. However, four long dwell times were found (sequential and multiple fixations) that were greater than 2,000 ms. One was to a CEVMS on a freeway in the day time, two were to the same standard billboard on a freeway once in the day and once at night; and one was to a standard billboard on an arterial at night. In Richmond, the results showed that drivers gazed more at CEVMS than at standard billboards at night; however, in Reading the drivers were equally likely to gaze towards CEVMS or standard billboards in day and night. The results of the study are consistent with research and theory on the control of gaze behavior in natural environments. The demands of the driving task tend to affect the driver’s self- regulation of gaze behavior. 17. Key Words Driver visual behavior, visual environment, billboards, eye tracking system, commercial electronic variable message signs, CEVMS, visual complexity 18. Distribution Statement No restrictions. 19. Security Classif. (of this report) Unclassified 20. Security Classif. (of this page) Unclassified 21. No. of Pages 22. Price Form DOT F 1700.7 (8-72) Reproduction of completed page authorized ii SI* (MODERN METRIC) CONVERSION FACTORS APPROXIMATE CONVERSIONS TO SI UNITS Symbol When You Know Multiply By To Find Symbol LENGTH in inches 25.4 millimeters mm ft feet 0.305 meters m yd yards 0.914 meters m mi miles 1.61 kilometers km AREA in2 square inches 645.2 square millimeters mm2 ft2 square feet 0.093 square meters m2 yd2 square yard 0.836 square meters m2 ac acres 0.405 hectares ha mi2 square miles 2.59 square kilometers km2 VOLUME fl oz fluid ounces 29.57 milliliters mL gal gallons 3.785 liters L ft3 cubic feet 0.028 cubic meters m3 yd3 cubic yards 0.765 cubic meters m3 NOTE: volumes greater than 1000 L shall be shown in m3 MASS oz ounces 28.35 grams g lb pounds 0.454 kilograms kg T short tons (2000 lb) 0.907 megagrams (or "metric ton") Mg (or "t") TEMPERATURE (exact degrees) oF Fahrenheit 5 (F-32)/9 Celsius oC or (F-32)/1.8 ILLUMINATION fc foot-candles 10.76 lux lx fl foot-Lamberts 3.426 candela/m2 cd/m2 FORCE and PRESSURE or STRESS lbf poundforce 4.45 newtons N lbf/in2 poundforce per square inch 6.89 kilopascals kPa APPROXIMATE CONVERSIONS FROM SI UNITS Symbol When You Know Multiply By To Find Symbol LENGTH mm millimeters 0.039 inches in m meters 3.28 feet ft m meters 1.09 yards yd km kilometers 0.621 miles mi AREA mm2 square millimeters 0.0016 square inches in2 m2 square meters 10.764 square feet ft2 m2 square meters 1.195 square yards yd2 ha hectares 2.47 acres ac km2 square kilometers 0.386 square miles mi2 VOLUME mL milliliters 0.034 fluid ounces fl oz L liters 0.264 gallons gal m3 cubic meters 35.314 cubic feet ft3 m3 cubic meters 1.307 cubic yards yd3 MASS g grams 0.035 ounces oz kg kilograms 2.202 pounds lb Mg (or "t") megagrams (or "metric ton") 1.103 short tons (2000 lb) T TEMPERATURE (exact degrees) oC Celsius 1.8C+32 Fahrenheit oF ILLUMINATION lx lux 0.0929 foot-candles fc iii TABLE OF CONTENTS EXECUTIVE SUMMARY _____________________________________________________1 INTRODUCTION_____________________________________________________________5 BACKGROUND ___________________________________________________________5 Post-Hoc Crash Studies____________________________________________________5 Field Investigations _______________________________________________________6 Laboratory Studies _______________________________________________________8 Summary _______________________________________________________________9 STUDY APPROACH _______________________________________________________9 Research Questions ______________________________________________________12 EXPERIMENTAL APPROACH _______________________________________________13 EXPERIMENTAL DESIGN OVERVIEW ____________________________________14 Site Selection __________________________________________________________14 READING __________________________________________________________________16 METHOD _______________________________________________________________16 Selection of Data Collection Zone Limits ____________________________________16 Advertising Conditions ___________________________________________________16 Photometric Measurement of Signs _________________________________________19 Visual Complexity ______________________________________________________20 Participants ____________________________________________________________21 Procedures _____________________________________________________________21 DATA REDUCTION ______________________________________________________23 Eye Tracking Measures___________________________________________________23 Other Measures _________________________________________________________25 RESULTS _______________________________________________________________26 Photometric Measurements ________________________________________________26 Visual Complexity ______________________________________________________27 Effects of Billboards on Gazes to the Road Ahead ______________________________28 Fixations to CEVMS and Standard Billboards _________________________________30 Comparison of Gazes to CEVMS and Standard Billboards _______________________36 Observation of Driver Behavior ____________________________________________36 Level of Service ________________________________________________________36 DISCUSSION OF READING RESULTS _____________________________________37 RICHMOND ________________________________________________________________40 METHOD _______________________________________________________________40 Selection of DCZ Limits __________________________________________________40 Advertising Type _______________________________________________________40 Photometric Measurement of Signs _________________________________________42 Visual Complexity ______________________________________________________42 Participants ____________________________________________________________43 Procedures _____________________________________________________________43 DATA REDUCTION ______________________________________________________44 Eye Tracking Measures___________________________________________________44 iv Other Measures _________________________________________________________44 RESULTS _______________________________________________________________44 Photometric Measurement of Signs _________________________________________44 Visual Complexity ______________________________________________________45 Effects of Billboards on Gazes to the Road Ahead ______________________________45 Fixations to CEVMS and Standard Billboards _________________________________47 Comparison of Gazes to CEVMS and Standard Billboards _______________________50 Observation of Driver Behavior ____________________________________________51 Level of Service ________________________________________________________51 DISCUSSION OF RICHMOND RESULTS ___________________________________51 GENERAL DISCUSSION _____________________________________________________53 CONCLUSIONS __________________________________________________________53 Do CEVMS attract drivers’ attention away from the forward roadway and other driving relevant stimuli? ________________________________________________________53 Do glances to CEVMS occur that would suggest a decrease in safety? ______________54 Do drivers look at CEVMS more than at standard billboards? ____________________54 SUMMARY ______________________________________________________________55 LIMITATIONS OF THE RESEARCH _______________________________________55 REFERENCES ______________________________________________________________57 v LIST OF FIGURES Figure 1. Eye tracking system camera placement. ____________________________________13 Figure 2. FHWA’s field research vehicle. __________________________________________14 Figure 3. DCZ with a target CEVMS on a freeway. ___________________________________17 Figure 4. DCZ with a target CEVMS on an arterial. __________________________________18 Figure 5. DCZ with a target standard billboard on a freeway. ___________________________18 Figure 6. DCZ with a target standard billboard on an arterial. ___________________________18 Figure 7. DCZ for the control condition on a freeway. ________________________________19 Figure 8. DCZ for the control condition on an arterial. ________________________________19 Figure 9. Screen capture showing static ROIs on a scene video output. ___________________23 Figure 10. Mean feature congestion as a function of advertising condition and road type (standard errors for the mean are included in the graph). ________________________27 Figure 11. Distribution of fixation duration for CEVMS in the daytime and nighttime. _______30 Figure 12. Distribution of fixation duration for standard billboards in the daytime and nighttime. ________________________________________________________________31 Figure 13. Distribution of fixation duration for road ahead (i.e., top and bottom road ahead ROIs) in the daytime and nighttime. ______________________________________31 Figure 14. Heat map for the start of a DCZ for a standard billboard at night on an arterial. __________________________________________________________________33 Figure 15. Heat map for the middle of a DCZ for a standard billboard at night on an arterial. __________________________________________________________________33 Figure 16. Heat map near the end of a DCZ for a standard billboard at night on an arterial. __________________________________________________________________33 Figure 17. Heat map for start of a DCZ for a standard billboard at night on a freeway. _______34 Figure 18. Heat map for middle of a DCZ for a standard billboard at night on a freeway. __________________________________________________________________34 Figure 19. Heat map near the end of a DCZ for a standard billboard at night on a freeway. __________________________________________________________________34 Figure 20. Heat map for the start of a DCZ for a standard billboard in the daytime on a freeway. ________________________________________________________________35 Figure 21. Heat map near the middle of a DCZ for a standard billboard in the daytime on a freeway. ______________________________________________________________35 Figure 22. Heat map near the end of DCZ for standard billboard in the daytime on a freeway. __________________________________________________________________35 Figure 23. Heat map at the end of DCZ for standard billboard in the daytime on a freeway. __________________________________________________________________35 Figure 24. Example of identified salient areas in a road scene based on bottom-up analysis. __________________________________________________________________38 Figure 25. Example of a CEVMS DCZ on a freeway. _________________________________41 Figure 26. Example of CEVMS DCZ an arterial. _____________________________________41 Figure 27. Example of a standard billboard DCZ on a freeway. _________________________41 Figure 28. Example of a standard billboard DCZ on an arterial. _________________________42 Figure 29. Example of a control DCZ on a freeway. __________________________________42 Figure 30. Example of a control DCZ on an arterial. __________________________________42 vi Figure 31. Mean feature congestion as a function of advertising condition and road type. _____________________________________________________________________45 Figure 32. Fixation duration for CEVMS in the day and at night. ________________________47 Figure 33. Fixation duration for standard billboards in the day and at night. ________________48 Figure 34. Fixation duration for the road ahead in the day and at night. ___________________48 Figure 35. Heat map for first fixation to CEVMS with long dwell time. ___________________49 Figure 36. Heat map for later fixations to CEVMS with long dwell time. __________________50 Figure 37. Heat map at end of fixations to CEVMS with long dwell time. _________________50 vii LIST OF TABLES Table 1. Distribution of CEVMS by roadway classification for various cities. ______________15 Table 2. Inventory of target billboards with relevant parameters. ________________________17 Table 3. Summary of luminance (cd/m 2 ) and contrast (Weber ratio) measurements. _________27 Table 4. The probability of gazing at the road ahead as a function of advertising condition and road type. _____________________________________________________28 Table 5. Probability of gazing at ROIs for the three advertising conditions on arterials and freeways. _____________________________________________________________29 Table 6. Level of service as a function of advertising type, road type, and time of day. _______37 Table 7. Inventory of target billboards in Richmond with relevant parameters. _____________40 Table 8. Summary of luminance (cd/m 2 ) and contrast (Weber ratio) measurements. _________44 Table 9. The probability of gazing at the road ahead as a function of advertising condition and road type. _____________________________________________________46 Table 10. Probability of gazing at ROIs for the three advertising conditions on arterials and freeways. ______________________________________________________46 Table 11. Estimated level of service as a function of advertising condition, road type, and time of day.____________________________________________________________51 viii LIST OF ACRONYMS AND SYMBOLS CEVMS Commercial Electronic Variable Message Sign EB Empirical Bayes DCZ Data Collection Zone ROI Region of Interest LED Light-Emitting Diode IR Infra-Red CCD Charge-Coupled Device MAPPS Multiple-Analysis of Psychophysical and Performance Signals GEE Generalized Estimating Equations FHWA Federal Highway Administration DOT Department of Transportation 1 EXECUTIVE SUMMARY This study examines where drivers look when driving past commercial electronic variable message signs (CEVMS), standard billboards, or no off-premise advertising. The results and conclusions are presented in response to the three research questions listed below: 1. Do CEVMS attract drivers’ attention away from the forward roadway and other driving- relevant stimuli? 2. Do glances to CEVMS occur that would suggest a decrease in safety? 3. Do drivers look at CEVMS more than at standard billboards? This study follows a Federal Highway Administration (FHWA) review of the literature on the possible distracting and safety effects of off-premise advertising and CEVMS in particular. The review considered laboratory studies, driving simulator studies, field research vehicle studies, and crash studies. The published literature indicated that there was no consistent evidence showing a safety or distraction effect due to off-premise advertising. However, the review also enumerated potential limitations in the previous research that may have resulted in the finding of no distraction effects for off-premise advertising. The study team recommended that additional research be conducted using instrumented vehicle research methods with eye tracking technology. The eyes are constantly moving and they fixate (focus on a specific object or area), perform saccades (eye movements to change the point of fixation), and engage in pursuit movements (track moving objects). It is during fixations that we take in detailed information about the environment. Eye tracking allows one to determine to what degree off-premise advertising may divert attention away from the forward roadway. A finding that areas containing CEVMS result in significantly more gazes to the billboards at a cost of not gazing toward the forward roadway would suggest a potential safety risk. In addition to measuring the degree to which CEVMS may distract from the forward roadway, an eye tracking device would allow an examination of the duration of fixations and dwell times (multiple sequential fixations) to CEVMS and standard billboards. Previous research conducted by the National Highway Traffic Safety Administration (NHTSA) led to the conclusion that taking your eyes off the road for 2 seconds or more presents a safety risk. Measuring fixations and dwell times to CEVMS and standard billboards would also allow a determination as to the degree to which these advertising signs lead to potentially unsafe gaze behavior. Most of the literature concerning eye gaze behavior in dynamic environments suggests that task demands tend to override visual salience (an object that stands out because of its physical properties) in determining attention allocation. When extended to driving, it would be expected that visual attention will be directed toward task-relevant areas and objects (e.g., the roadway, other vehicles, speed limit signs) and that other salient objects, such as billboards, would not necessarily capture attention. However, driving is a somewhat automatic process and conditions generally do not require constant, undivided attention. As a result, salient stimuli, such as CEVMS, might capture driver attention and produce an unwanted increase in driver distraction. The present study addresses this concern. 2 This study used an instrumented vehicle with an eye tracking system to measure where drivers were looking when driving past CEVMS and standard billboards. The CEVMS and standard billboards were measured with respect to luminance, location, size, and other relevant variables to characterize these visual stimuli extensively. Unlike previous studies on digital billboards, the present study examined CEVMS as deployed in two United States cities. These billboards did not contain dynamic video or other dynamic elements, but changed content approximately every 8 to 10 seconds. The eye tracking system had nearly a 2-degree level of resolution that provided significantly more accuracy in determining what objects the drivers were looking at compared to an earlier naturalistic driving study. This study assessed two data collection efforts that employed the same methodology in two cities. In each city, the study examined eye glance behavior to four CEVMS, two on arterials and two on freeways. There were an equal number of signs on the left and right side of the road for arterials and freeways. The standard billboards were selected for comparison with CEVMS such that one standard billboard environment matched as closely as possible that of each of the CEVMS. Two control locations were selected that did not contain off-premise advertising, one on an arterial and the other on a freeway. This resulted in 10 data collection zones in each city that were approximately 1,000 feet in length (the distance from the start of the data collection zone to the point that the CEVMS or standard billboard disappeared from the data collection video). In Reading, Pennsylvania, 14 participants drove at night and 17 drove during the day. In Richmond, Virginia, 10 participants drove at night and 14 drove during the day. Calibration of the eye tracking system, practice drive, and the data collection drive took approximately 2 hours per participant to accomplish. The following is a summary of the study results and conclusions presented in reference to the three research questions the study aimed to address. Do CEVMS attract drivers’ attention away from the forward roadway and other driving relevant stimuli? • On average, the drivers in this study devoted between 73 and 85 percent of their visual attention to the road ahead for both CEVMS and standard billboards. This range is consistent with earlier field research studies. In the present study, the presence of CEVMS did not appear to be related to a decrease in looking toward the road ahead. Do glances to CEVMS occur that would suggest a decrease in safety? • The average fixation duration to CEVMS was 379 ms and to standard billboards it was 335 ms across the two cities. The average fixation durations to CEVMS and standard billboards were similar to the average fixation duration to the road ahead. • The longest fixation to a CEVMS was 1,335 ms and to a standard billboard it was 1,284 ms. The current widely accepted threshold for durations of glances away from the road ahead that result in higher crash risk is 2,000 ms. This value comes from a NHTSA 3 naturalistic driving study that showed a significant increase in crash odds when glances away from the road ahead were 2,000 ms or longer. • Four dwell times (aggregate of consecutive fixations to the same object) greater than 2,000 ms were observed across the two studies. Three were to standard billboards and one was to a CEVMS. The long dwell time to the CEVMS occurred in the daytime to a billboard viewable from a freeway. Review of the video data for these four long dwell times showed that the signs were not far from the forward view while participant’s gaze dwelled on them. Therefore, the drivers still had access to information about what was in front of them through peripheral vision. • The results did not provide evidence indicating that CEVMS, as deployed and tested in the two selected cities, were associated with unacceptably long glances away from the road. When dwell times longer than the currently accepted threshold of 2,000 ms occurred, the road ahead was still in the driver’s field of view. This was the case for both CEVMS and standard billboards. Do drivers look at CEVMS more than at standard billboards? • When comparing the probability of a gaze at a CEVMS versus a standard billboard, the drivers in this study were generally more likely to gaze at CEVMS than at standard billboards. However, some variability occurred between the two locations and between the types of roadway (arterial or freeway). • In Reading, when considering the proportion of time spent looking at billboards, the participants looked more often at CEVMS than at standard billboards when on arterials (63 percent to CEVMS and 37 percent to a standard billboard), whereas they looked more often at standard billboards when on freeways (33 percent to CEVMS and 67 percent to a standard billboard). In Richmond, the drivers looked at CEVMS more than standard billboards no matter the type of road they were on, but as in Reading, the preference for gazing at CEVMS was greater on arterials (68 percent to CEVMS and 32 percent to standard billboards) than on freeways (55 percent to CEVMS and 45 percent to standard billboards). When a gaze was to an off-premise advertising sign, the drivers were generally more likely to gaze at a CEVMS than at a standard billboard. • In Richmond, the drivers showed a preference for gazing at CEVMS versus standard billboards at night, but in Reading the time of day did not affect gaze behavior. In Richmond, drivers gazed at CEVMS 71 percent and at standard billboards 29 percent at night. On the other hand, in the day the drivers gazed at CEVMS 52 percent and at standard billboards 48 percent. • In Reading, the average gaze dwell time for CEVMS was 981 ms and for standard billboards it was 1,386 ms. The difference in these average dwell times was not statistically significant. In contrast, the average dwell times to CEVMS and standard billboards were significantly different in Richmond (1,096 ms and 674 ms, respectively). 4 The present data suggest that the drivers in this study directed the majority of their visual attention to areas of the roadway that were relevant to the task at hand (e.g., the driving task). Furthermore, it is possible, and likely, that in the time that the drivers looked away from the forward roadway, they may have elected to glance at other objects in the surrounding environment (in the absence of billboards) that were not relevant to the driving task. When billboards were present, the drivers in this study sometimes looked at them, but not such that overall attention to the forward roadway decreased. It also should be noted that, like other studies in the available literature, this study adds to the knowledge base on the issues examined, but does not present definitive answers to the research questions investigated. 5 INTRODUCTION “The primary responsibility of the driver is to operate a motor vehicle safely. The task of driving requires full attention and focus. Drivers should resist engaging in any activity that takes their eyes and attention off of the road for more than a couple of seconds. In some circumstances even a second or two can make all the difference in a driver being able to avoid a crash.” – US Department of Transportation (1) The advent of electronic billboard technologies, in particular the digital Light-Emitting Diode (LED) billboard, has prompted a reevaluation of regulations for controlling outdoor advertising. An attractive quality of these LED billboards, which are hereafter referred to as Commercial Electronic Variable Message Signs (CEVMS), is that advertisements can change almost instantly. Furthermore, outdoor advertising companies can make these changes from a central remote office. Of concern is whether or not CEVMS may attract drivers’ attention away from the primary task (driving) in a way that compromises safety. The current Federal Highway Administration (FHWA) guidance recommends that CEVMS should not change content more frequently than once every 8 seconds. (2) However, according to Scenic America, the basis of the safety concern is that the “…distinguishing trait…” of a CEVMS “… is that it can vary while a driver watches it, in a setting in which that variation is likely to attract the drivers’ attention away from the roadway.” (3) This study was conducted to provide the FHWA with data to determine if CEVMS capture visual attention differently than standard off-premise advertising billboards. BACKGROUND A 2009 review of the literature by Molino et al. for the FHWA failed to find convincing empirical evidence that CEVMS, as currently implemented, constitutes a safety risk greater than that of conventional vinyl billboards. (4) A great deal of work has been focused in this area, but the findings of these studies have been mixed. (4,5) A summary of the key past findings is presented here, but the reader is referred to Molino et al. for a comprehensive review of studies prior to 2008. (4) Post-Hoc Crash Studies Post-hoc crash studies use reviews of police traffic collision reports or statistical summaries of such reports in an effort to understand the causes of crashes that have taken place in the vicinity of some change to the roadside environment. In the present case, the change of concern is the introduction of CEVMS to the roadside or the replacement of conventional billboards with CEVMS. The literature review conducted by Molino et al. did not find compelling evidence for a distraction effect attributable to CEVMS. (4) The authors concluded that all post-hoc crash studies are subject to certain weaknesses, most of which are difficult to overcome. For example, the vast majority of crashes are never reported to police; thus, such studies are likely to underreport crashes. Also, when crashes are caused by factors such as driver distraction or inattention, the involved driver may be unwilling or unable to report these factors to a police investigator. 6 Another weakness is that police, under time pressure, are rarely able to investigate the true root causes of crashes unless they involve serious injury, death, or extensive property damage. Furthermore, to have confidence in the results, such studies need to collect comparable data before and after the change, and, in the after phase, at equivalent but unaffected roadway sections. Since crashes are infrequent events, data collection needs to span extended periods of time both before and after introduction of the change. Few studies are able to obtain such extensive data. Two recent studies by Tantala and Tantala examined the relationship between the presence of CEVMS and crash statistics in Richmond, Virginia, and Reading, Pennsylvania. (6,7) For the Richmond area, 7 years of crash data at 10 locations with CEVMS were included in the analyses. The study used a before-after methodology where most sites originally contained vinyl billboards (before) that were converted to CEVMS (after). The quantity of crash data was not the same for all locations and ranged from 1 year before/after to 3 years before/after. The study employed the Empirical Bayes (EB) method to analyze the data. (8) The results indicated that the total number of crashes observed was consistent with what would be statistically expected with or without the introduction of CEVMS. The analysis approach for Reading locations was much the same as for Richmond other than there were 20 rather than 10 CEVMS and 8 years of crash statistics. The EB method showed results for Reading that were very similar to those of Richmond. The studies by Tantala and Tantala appear to address many of the concerns from Molino et al. regarding the weaknesses and issues associated with crash studies. (4,6,7) For example, they include crash comparisons for locations within multiple distances of each CEVMS to address concerns about the visual range used in previous analyses. They used EB analysis techniques to correct for regression-to-mean bias. Also, the EB method would better reflect crash rate changes due to changes in average daily traffic and the interactions of these with the roadway features that were coded in the model. The studies followed approaches that are commonly used in post- hoc crash studies, though the results would have been strengthened by including before-after results for non-CEVMS locations as a control group. Field Investigations Field investigations include unobtrusive observation, naturalistic driving studies, on-road instrumented vehicle investigations, test track experiments, driver interviews, surveys, and questionnaires. The following focuses on relevant studies that employed naturalistic driving and on-road instrumented vehicle research methods. Lee, McElheny, and Gibbons undertook an on-road instrumented vehicle study on Interstate and local roads near Cleveland, Ohio. (9) The study looked at driver glance behavior in the vicinity of digital billboards, conventional billboards, comparison sites (sites with buildings and other signs, including digital signs), and control sites (those without similar signage). The results showed that there were no differences in the overall glance patterns (percent eyes-on-road and overall number of glances) between the different sites. Drivers also did not glance more frequently in the direction of digital billboards than in the direction of other event types (conventional billboards, comparison events, and baseline events) but drivers did take longer glances in the direction of digital billboards and comparison sites than in the direction of conventional billboards and baseline sites. However, the mean glance length toward the digital billboards was less than 7 1,000 ms. It is important to note that this study employed a video-based approach for examining drivers’ visual behavior, which has an accuracy of no better than 20 degrees. (10) While this technique is likely to be effective in assessing gross eye movements and looks that are away from the road ahead, it may not have sufficient resolution to discriminate what specific object the driver is looking at outside of the vehicle. Beijer, Smiley, and Eizenman evaluated driver glances toward four different types of roadside advertising signs on roads in the Toronto, Canada, area. (11) The four types of signs were: (a) billboard signs with static advertisements; (b) billboard advertisements placed on vertical rollers that could rotate to show one of three advertisements in succession; (c) scrolling text signs with a minor active component, which usually consisted of a small strip of lights that formed words scrolling across the screen or, in some cases, a larger area capable of displaying text but not video; and (d) signs with video images that had a color screen capable of displaying both moving text and moving images. The study employed an on-road instrumented vehicle with a head- mounted eye tracking device. The researchers found no significant differences in average glance duration or the maximum glance duration for the various sign types; however, the number of glances was significantly lower for billboard signs than for the roller bar, scrolling text, and video signs. Smiley, Smahel, and Eizenman conducted a field driving study that employed an eye tracking system that recorded drivers’ eye movements as participants drove past video signs located at three downtown intersections and along an urban expressway. (12) The study route included static billboards and video advertising. The results of the study showed that on average 76 percent of glances were to the road ahead. Glances at advertising, including static billboards and video signs, constituted 1.2 percent of total glances. The mean glance durations for advertising signs were between 500 ms and 750 ms, although there were a few glances of about 1,400 ms in duration. Video signs were not more likely than static commercial signs to be looked at when headways were short; in fact, the reverse was the case. Furthermore, the number of glances per individual video sign was small, and statistically significant differences in looking behavior were not found. Kettwich, Kartsen, Klinger, and Lemmer conducted a field study where drivers’ gaze behavior was measured with an eye tracking system. (13) Sixteen participants drove an 11.5 mile (18.5 km) route comprised of highways, arterial roads, main roads, and one-way streets in Karlsruhe, Germany. The route contained advertising pillars, event posters, company logos, and video screens. Mean gaze duration for the four types of advertising was computed for periods when the vehicle was in motion and when it was stopped. Gaze duration while driving for all types of advertisements was under 1,000 ms. On the other hand, while the vehicle was stopped, the mean gaze duration for video screen advertisements was 2,750 ms. The study showed a significant difference between gaze duration while driving and while stationary: gaze duration was affected by the task at hand. That is, drivers tended to gaze longer while the car was stopped and there were few driving task demands. The previously mentioned studies estimated the duration of glances to advertising and computed mean values of less than 1,000 ms. Klauer et al., in his analysis of the 100-Car Naturalistic Driving Study, concluded that glances away from the roadway for any purpose lasting more than 2,000 ms increase near-crash/crash risk by at least two times that of normal, baseline driving. (14) 8 Klauer et al. also indicated that short, brief glances away from the forward roadway for the purpose of scanning the driving environment are safe and actually decrease near-crash/crash risk. (14) Using devices in a vehicle that draw visual attention away from the forward roadway for more than 2,000 ms (e.g., texting) is incompatible with safe driving. However, for external stimuli, especially those near the roadway, the evaluation of eye glances with respect to safety is less clear since peripheral vision would allow the driver to still have visual access to the forward roadway. Laboratory Studies Laboratory investigations related to roadway safety can be classified into several categories: driving simulations, non-driving-simulator laboratory testing, and focus groups. The review of relevant laboratory studies by Molino et al. did not show conclusive evidence regarding the distracting effects of CEVMS. (4) Moreover, the authors concluded that present driving simulators do not have sufficient visual dynamic range, image resolution, and contrast ratio capability to produce the compelling visual effect of a bright, photo-realistic LED-based CEVMS against a natural background scene. The following is a discussion of a driving simulator study conducted after the publication of Molino et al. (4) The study focused on the effects of advertising on driver visual behavior. Chattington, Reed, Basacik, Flint, and Parkes conducted a driving simulator study in the United Kingdom (UK) to evaluate the effects of static and video advertising on driver glance behavior. (15) The researchers examined the effects of advertisement position relative to the road (left, right, center on an overhead gantry, and in all three locations simultaneously), type of advertisement (static or video), and exposure duration of the advertisement. (The paper does not provide these durations in terms of time or distance. The exposure duration had to do with the amount of time or distance that the sign would be visible to the driver.) For the advertisements presented on the left side of the road (recall that drivers travel in the left lane in the UK), mean glance durations for static and video advertisements were significantly longer (approximately 650 to 750 ms) when drivers experienced long advertisement exposure as opposed to medium and short exposures. Drivers looked more at video advertisements (about 2 percent on average of the total duration recorded) than at static advertisements (about 0.75 percent on average). In addition, the location of the advertisements had an effect on glance behavior. When advertisements were located in the center of the road or in all three positions simultaneously, the glance durations were about 1,000 ms and were significantly longer than for signs placed on the right or left side of the road. For advertisements placed on the left side of the road, there was a significant difference in glance duration between static (about 400 ms) and video (about 800 ms). Advertisement position also had an effect on the proportion of time that a driver spent looking at an advertisement. The percentage of time looking at advertisements was greatest when signs were placed in all three locations, followed by center location signs, then the left location signs, and finally the right location signs. Drivers looked more at the video advertisements relative to the static advertisements when they were placed in all three locations, placed on the left, and placed on the right side of the road. The center placement did not show a significant difference in percent of time spent looking between static and video. 9 Summary The results from these key studies offer some insight into whether CEVMS pose a visual distraction threat. However, these same studies also reveal some inconsistent findings and potential methodological issues that are addressed in the current study. The studies conducted by Smiley et al. showed drivers glanced forward at the roadway about 76 percent of the time in the presence of video and dynamic signs where a few long glances of approximately 1,400 ms were observed. (12) However, the video and dynamic signs used in these studies portray moving objects that are not present in CEVMS as deployed in the United States. In another field study employing eye tracking, Kettwich et al. found that gaze duration while driving for all types of advertisements that they evaluated was less than 1,000 ms; however, when the vehicle was stopped, mean gaze duration for advertising was as high as 2,750 ms. (16) Collectively, these studies did not demonstrate that the advertising signs detracted from drivers’ glances forward at the roadway in a substantive manner while the vehicle was moving. In contrast, the simulator study by Chattington et al. demonstrated that dynamic signs showing moving video or other dynamic elements may draw attention away from the roadway. (15) Furthermore, the location of the advertising sign on the road is an important factor in drawing drivers’ visual attention. Advertisements with moving video placed in the center of the roadway on an overhead gantry or in all three positions (right, left, and in the center) simultaneously are very likely to draw glances from drivers. Finally, in a study that examined CEVMS as deployed in the United States, Lee et al. did not show any significant effects of CEVMS on driver glance behavior. (9) However, the methodology that was used likely did not employ sufficient sensitivity to determine at what specific object in the environment a driver was looking. None of these studies combined all necessary factors to address the current CEVMS situation in the United States. Those studies that used eye tracking on real roads had animated and video- based signs, which are not reflective of current off-premise CEVMS practice in the United States. STUDY APPROACH Based on an extensive review of the literature, Molino et al. concluded that the most effective method to use in an evaluation of the effects of CEVMS on driver visual behavior was the instrumented field vehicle method that incorporated an eye tracking system. (4) The present study employed such an instrumented field vehicle with an eye tracking system and examined the degree to which CEVMS attract drivers’ attention away from the forward roadway. The following presents a brief overview and discussion of studies using eye tracking methodology with complex visual stimuli, especially in natural environments (walking, driving, etc.). The review by Molino et al. recommended the use of this type of technology and method; however, a discussion laying out technical and theoretical issues underlying the use of eye tracking methods was not presented. (4) This background is important for the interpretation of the results of the studies conducted here. 10 Standard and digital billboards are often salient stimuli in the driving environment, which may make them conspicuous. Cole and Hughes define attention conspicuity as the extent to which a stimulus is sufficiently prominent in the driving environment to capture attention. Further, Cole and Hughes state that attention conspicuity is a function of size, color, brightness, contrast relative to surroundings, and dynamic components such as movement and change. (17) It is clear that under certain circumstances image salience or conspicuity can provide a good explanation of how humans orient their attention. At any given moment a large number of stimuli reach our senses, but only a limited number of them are selected for further processing. In general, attention can be focused on a stimulus because it is important for achieving some goal, or because the properties of the stimulus can attract the attention of the observer independent of their intentions (e.g., a car horn may elicit an orienting response). When the focus of attention is goal directed, it is referred to as top-down. When the focus of attention is principally a function of stimulus attributes, it is referred to as bottom-up. (18) In general, billboards (either standard or CEVMS) are not relevant to the driving task but are presumably designed to be salient stimuli in the environment where they may draw a driver’s attention. The question is to what degree CEVMS draw a driver’s attention away from driving- relevant stimuli (e.g., road ahead, mirrors, and speedometer) and is this different from a standard billboard? In his review of the literature Wachtel leads one to consider CEVMS as stimuli in the environment where attention to them would be drawn in a bottom-up manner; that is, the salience of the billboards would make them stand out relative to other stimuli in the environment and drivers would reflexively look at these signs. (19) Wachtel’s conclusions were in reference to research by Theeuwees who employed simple letter stimulus arrays in a laboratory task. (20) Research using simple visual stimuli in a laboratory environment are very useful for testing different theories of perception, but often lack direct application to tasks such as driving. The following discusses research using complex visual stimuli and tasks that are more relevant to natural vision as experienced in the driving task. A recent review of stimulus salience and eye guidance by Tatler et al. shows that most of the evidence for the capture of attention by the conspicuity of stimuli comes from research in which the stimulus is a simple visual search array or in which the target is uniquely defined by simple visual features. (21) In other words, these are laboratory studies that use letters, arrays of letters, or simple geometric patterns as the stimuli. Pure salience-based models are capable of predicting eye movement endpoint in simple displays, but are less successful for more complex scenes that contain task-relevant and task-irrelevant salient areas. (22,23) Research by Henderson et al. using photographs of actual scenes showed that subjects looked at non-salient scene regions containing a search target and rarely looked at salient non-task-relevant regions of the scenes. (24) Salience of the stimulus alone was not a good predictor of where participants looked. Additional research by Henderson using photographs of real world scenes also showed that subjects fixated on regions of the pictures that provided task-relevant information rather than visually salient regions with no task-relevant information. However, Henderson acknowledges that static pictures have many shortcomings when used as surrogates for real environments. (25) 11 Land’s review of eye movements in dynamic environments concluded that the eyes are proactive and typically seek out information required in the second before each new activity commences. (26) Specific tasks (e.g., driving) have characteristic but flexible patterns of eye movement that accompany them, and these patterns are similar between individuals. Land concluded that the eyes rarely visit objects that are irrelevant to the task, and the conspicuity of objects is less important than the objects’ roles in the task. In a subsequent review of eye movement and natural behavior, Land concluded that in a task that requires fixation on a sequence of specific objects, the capture of gaze by irrelevant salient objects would, in general, be an obtrusive nuisance. (22) The literature examining gaze control under natural behavior suggests that it is principally top- down driven, or intentional. (24,25,26,22,21,27) However, top-down processing does not explain all gaze control or eye movements. For example, imagine driving down a two-lane country road and a deer jumps into the road. It is most likely that you will attend and react to this deer. Unplanned or unexpected stimuli capture our attention as we engage in complex natural tasks. Research by Jovancevic-Misic and Hayhoe showed that human gaze patterns are sensitive to the probabilistic nature of the environment. (28) In this study, participants’ eye movement behavior was observed while walking among other pedestrians. The other pedestrians were confederates and were either safe, risky, or rogue pedestrians. When the study began, the risky pedestrian took a collision course with the participant 50 percent of the time, and the rogue pedestrian always assumed a collision course as he approached the participant, whereas the safe pedestrian never took a collision course. Midway through the study the rogue and safe pedestrians exchanged roles but the risky pedestrian role remained the same. The participants were not informed about the behavior of the other pedestrians. Participants were asked to follow a circular path for several laps and to avoid other pedestrians. The study showed that the participants modified their gaze behavior in response to the change in the other pedestrians’ behavior. Jovancevic-Misic concluded that participants learned new priorities for gaze allocation within a few encounters and looked both sooner and longer at potentially dangerous pedestrians. (28) Gaze behavior in natural environments is affected by expectations that are derived through long- term learning. Using a virtual driving environment, Shinoda et al. asked participants to look for stop signs while driving an urban route. (29) Approximately 45 percent of the fixations fell in the general area of intersections during the simulated drive, and participants were more likely to detect stop signs placed near intersections than those placed in the middle of a block. Over time, drivers have learned that stop signs are more likely to appear near intersections and, as a result, drivers prioritize their allocation of gazes to these areas of the roadway. The Tatler et al. review of the literature concludes that in natural vision, a consistent set of principles underlies eye guidance. These principles include relevance or reward potential, uncertainty about the state of the environment, and learned models of the environment. (21) Salience of environmental stimuli alone typically does not explain most eye gaze behavior in naturalistic environments. In sum, most of the literature concerning eye gaze behavior in dynamic environments suggests that task demands tend to override visual salience in determining attention allocation. When extended to driving, it would be expected that visual attention will be directed toward task- relevant areas and objects (e.g., the roadway, other vehicles, speed limit signs, etc.) and other 12 salient objects, such as billboards, will not necessarily capture attention. However, driving is a somewhat automatic process and conditions generally do not require constant undivided attention. As a result, salient stimuli, such as CEVMS, might capture driver attention and provide an unwarranted increase in driver distraction. The present study addresses this concern. Research Questions The present research evaluated the effects of CEVMS on driver visual behavior under actual roadway conditions in the daytime and at night. Roads containing CEVMS, standard billboards, and areas not containing off-premise advertising were selected. The CEVMS and standard billboards were measured with respect to luminance, location, size, and other relevant visual characteristics. The present study examined CEVMS as deployed in two United States cities. Unlike previous studies, the signs did not contain dynamic video or other dynamic elements. In addition, the eye tracking system used in this study has approximately a 2-degree level of resolution. This provided significantly more accuracy in determining what objects the drivers were looking at than in previous on-road studies examining looking behavior (recall that Lee et al. used video recordings of drivers’ faces that, at best, examined gross eye movements). (9) Two studies are reported. Each study was conducted in a different city. The two studies employed the same methodology. The studies’ primary research questions were: 1. Do CEVMS attract drivers’ attention away from the forward roadway and other driving relevant stimuli? 2. Do glances to CEVMS occur that would suggest a decrease in safety? 3. Do drivers look at CEVMS more than at standard billboards? 13 EXPERIMENTAL APPROACH The study used a field research vehicle equipped with a non-intrusive eye tracking system. The vehicle was a 2007 Jeep® Grand Cherokee Sport Utility Vehicle. The eye tracking system used (SmartEye® vehicle-mounted infrared (IR) eye-movement measuring system) is shown in figure 1. (30) The system consists of two IR light sources and three face cameras mounted on the dashboard of the vehicle. The cameras and light sources are small in size, and are not attached to the driver in any manner. The face cameras are synchronized to the IR light sources and are used to determine the head position and gaze direction of the driver. Figure 1. Eye tracking system camera placement. As a part of this eye tracking system, the vehicle was outfitted with a three-camera panoramic scene monitoring system for capturing the forward driving scene. The scene cameras were mounted on the roof of the vehicle directly above the driver’s head position. The three cameras together provided an 80-degree wide by 40-degree high field of forward view. The scene cameras captured the forward view area available to the driver through the left side of the windshield and a portion of the right side of the windshield. The area visible to the driver through the rightmost area of the windshield was not captured by the scene cameras. The vehicle was also outfitted with equipment to record GPS position, vehicle speed, and vehicle acceleration. The equipment also recorded events entered by an experimenter and synchronized those events with the eye tracking and vehicle data. The research vehicle is pictured in figure 2. 14 Figure 2. FHWA’s field research vehicle. EXPERIMENTAL DESIGN OVERVIEW The approach entailed the use of the instrumented vehicle in which drivers navigated routes in cities that presented CEVMS and standard billboards as well as areas without off-premise advertising. The participants were instructed to drive the routes as they normally would. The drivers were not informed that the study was about outdoor advertising, but rather that it was about examining drivers’ glance behavior as they followed route guidance directions. Site Selection More than 40 cities were evaluated in the selection of the test sites. Locations with CEVMS displays were identified using a variety of resources that included State department of transportation contacts, advertising company Web sites, and a popular geographic information system. A matrix was developed that listed the number of CEVMS in each city. For each site, the number of CEVMS along limited access and arterial roadways was determined. One criterion for site selection was whether the location had practical routes that pass by a number of CEVMS as well as standard off-premise billboards and could be driven in about 30 minutes. Other considerations included access to vehicle maintenance personnel/facilities, proximity to research facilities, and ease of participant recruitment. Two cities were selected: Reading, and Richmond. Table 1 presents the 16 cities that were included on the final list of potential study sites. 15 Table 1. Distribution of CEVMS by roadway classification for various cities. State Area Limited Access Arterial Other (1) Total VA Richmond 4 7 0 11 PA Reading 7 11 0 18 VA Roanoke 0 11 0 11 PA Pittsburgh 0 0 15 15 TX San Antonio 7 2 6 15 WI Milwaukee 14 2 0 16 AZ Phoenix 10 6 0 16 MN St. Paul/Minneapolis 8 5 3 16 TN Nashville 7 10 0 17 FL Tampa-St. Petersburg 7 11 0 18 NM Albuquerque 0 19 1 20 PA Scranton-Wilkes Barre 7 14 1 22 OH Columbus 1 22 0 23 GA Atlanta 13 11 0 24 IL Chicago 22 2 1 25 CA Los Angeles 3 71 4 78 (1) Other includes roadways classified as both limited access and arterial or instances where the road classification was unknown. Source: www.lamar.com and www.clearchannel.com In both test cities, the following independent variables were evaluated: • The type of advertising. This included CEVMS, standard billboards, and no off-premise advertising. (It should be noted that in areas with no off-premise advertising, it was still possible to encounter on-premise advertising; e.g., for gas stations, restaurants, and other miscellaneous stores and shops.) • Time of day. This included driving in the daytime and at night. • The functional class of roadways in which off-premise advertising signs were located. Roads were classified as either freeway or arterial. It was observed that the different road classes were correlated with the presence of other visual information that could affect the driver’s glance behavior. For example, the visual environment on arterials may be more complex or cluttered than on freeways because of the close proximity of buildings, driveways, and on-premise advertising, etc. 16 READING The first on-road study was conducted in Reading. This study examined the type of advertising (CEVMS, standard billboard, or no off-premise advertising), time of day (day or night) and road type (freeway or arterial) as independent variables. Eye tracking was used to assess where participants gazed and for how long while driving. The luminance and contrast of the advertising signs were measured to characterize the billboards in the current study. METHOD Selection of Data Collection Zone Limits Data collection zones (DCZ) were defined on the routes that participants drove where detailed analyses of the eye tracking data were planned. The DCZ were identified that contained a CEVMS, a standard billboard, or no off-premise advertising. The rationale for selecting the DCZ limits took into account the geometry of the roadway (e.g., road curvature or obstructions that blocked view of billboards) and the capabilities of the eye tracking system (2 degrees of resolution). At a distance of 960 ft (292.61 m), the average billboard in Reading was 12.8 ft (3.90 m) by 36.9 ft (11.25 m) and would subtend a horizontal visual angle of 2.20 degrees and a vertical visual angle of 0.76 degrees, and thus glances to the billboard would just be resolvable by an eye tracking system with 2 degrees of accuracy. Therefore 960 ft was chosen as the maximum distance from billboards at which a DCZ would begin. If the target billboard was not visible from 960 ft (292.61 m) due to roadway geometry or other visual obstructions, such as trees or an overpass, the DCZ was shortened to a distance that prevented these objects from interfering with the driver’s vision of the billboard. In DCZs with target off-premise billboards, the end of the DCZ was marked when the target billboard left the view of the scene camera. If the area contained no off-premise advertising, the end of the DCZ was defined by a physical landmark leaving the view of the eye tracking systems’ scene camera. Table 2 shows the data collection zone limits used in this study. Advertising Conditions The type of advertising present in DCZs was examined as an independent variable. DCZs fell into one of the following categories, which are listed in the second column of table 2: • CEVMS. These were DCZs that contained one target CEVMS. Two CEVMS DCZs were located on freeways and two were located on arterials. Figure 3 and figure 4 show examples of CEVMS DCZs with the CEVMS highlighted in the pictures. • Standard billboard. These were DCZs that contained one target standard billboard. Two standard billboard DCZs were located on freeways and two were located on arterials. Figure 5 and figure 6 show examples of standard billboard DCZs; the standard billboards are highlighted in the pictures. 17 • No off-premise advertising conditions. These DCZs contained no off-premise advertising. One of these DCZs was on a freeway (see figure 7) and the other was on an arterial (see figure 8). Table 2. Inventory of target billboards with relevant parameters. DCZ Advertising Type Copy Dimensions (ft) Side of Road Setback from Road (ft) Other Standard Billboards Approach Length (ft) Type of Roadway 1 CONTROL N/A N/A N/A N/A 786 Freeway 6 CONTROL N/A N/A N/A N/A 308 Arterial 3 CEVMS 10'6" x 22'9" L 12 0 375 Arterial 5 CEVMS 14'0" x 48'0" L 133 1 853 Freeway 9 CEVMS 10'6" x 22'9" R 43 0 537 Arterial 10 CEVMS 14'0" x 48'0" R 133 1 991 Freeway 2 Standard 14'0" x 48'0" L 20 0 644 Arterial 7 Standard 14'0" x 48'0" R 35 1 774 Freeway 8 Standard 10'6" x 22'9" R 40 1 833 Arterial 4 Standard 14'0" x 48'0" L 10 0 770 Freeway *N/A indicates that there were no off-premise advertising in these areas and these values are undefined. Figure 3. DCZ with a target CEVMS on a freeway. 18 Figure 4. DCZ with a target CEVMS on an arterial. Figure 5. DCZ with a target standard billboard on a freeway. Figure 6. DCZ with a target standard billboard on an arterial. 19 Figure 7. DCZ for the control condition on a freeway. Figure 8. DCZ for the control condition on an arterial. Photometric Measurement of Signs Two primary metrics were used to describe the photometric characteristics of a sample of the CEVMS and standard billboards present at each location: luminance (cd/m 2 ) and contrast (Weber contrast ratio). Photometric Equipment Luminance was measured with a Radiant Imaging ProMetric 1600 Charge-Coupled Device (CCD) photometer with both a 50 mm and a 300 mm lenses. The CCD photometer provided a method of capturing the luminance of an entire scene at one time. The photometric sensors were mounted in a vehicle of similar size to the eye tracking research vehicle. The photometer was located in the experimental vehicle as close to the driver’s position as possible and was connected to a laptop computer that stored data as the images were acquired. Measurement Methodology Images of the billboards were acquired using the photometer manufacturer’s software. The software provided the mean luminance of each billboard message. To prevent overexposure of 20 images in daylight, neutral density filters were manually affixed to the photometer lens and the luminance values were scaled appropriately. Standard billboards were typically measured only once; however, for CEVMS multiple measures were taken to account for changing content. Photometric measurements were taken during day and night. Measurements were taken by centering the billboard in the photometer’s field of view with approximately the equivalent of the width of the billboard on each side and the equivalent of the billboard height above and below the sign. The areas outside of the billboards were included to enable contrast calculations. Standard billboards were assessed at a mean distance of 284 ft (ranging from 570 ft to 43 ft). The CEVMS were assessed at a mean distance of 479 ft (ranging from 972 ft to 220 ft). To include the background regions of appropriate size, the close measurement distances required the use of the 50 mm lens whereas measurements made from longer distances required the 300 mm lens. A significant determinant of the measurement locations was the availability of accessible and safe places from which to measure. The Weber contrast ratio was used because it characterizes a billboard as having negative or positive contrast when compared to its background area. (31) A negative contrast indicates the background areas have a higher mean luminance than the target billboard. A positive contrast indicates the target billboard has a higher mean luminance than the background. Overall, the absolute value of a contrast ratio simply indicates a difference in luminance between an item and its background. From a perceptual perspective luminance and contrast are directly related to the perception of brightness. For example, two signs with equal luminance may be perceived differently with respect to brightness because of differences in contrast. Visual Complexity Regan, Young, Lee and Gordon presented a taxonomic description of the various sources of driver distraction. (32) Potential sources of distraction were discussed in terms of: things brought into the vehicle; vehicle systems; vehicle occupants; moving objects or animals in the vehicle; internalized activity; and external objects, events, or activities. The external objects may include buildings, construction zones, billboards, road signs, vehicles, and so on. Focusing on the potential for information outside the vehicle to attract (or distract) the driver’s attention, Horberry and Edquist developed a taxonomy for out-of-the-vehicle visual information. This suggested taxonomy includes four groupings of visual information: built roadway, situational entities, natural environment, and built environment. (33) These two taxonomies provide an organizational structure for conducting research; however, they do not currently provide a systematic or quantitative way of classifying the level of clutter or visual complexity present in a visual scene. The method proposed by Rozenholtz, Li, and Nakano provides quantitative and perhaps reliable measures of visual clutter. (34) Their approach measures the feature congestion in a visual image. The implementation of the feature congestion measure involves four stages: (1) compute local feature covariance at multiple scales and compute the volume of the local covariance ellipsoid, (2) combine clutter across scale, (3) combine clutter across feature types, and (4) pool over space to get a single measure of clutter for each input image. The implementation that was used employed color, orientation and luminance contrast as features. Presumably, less cluttered 21 images can be visually coded more efficiently than cluttered images. For example, visual clutter can cause decreased recognition performance and greater difficulty in performing visual search. (35) Participants In the present study participants were recruited at public libraries in the Reading area. A table was set up so that recruiters could discuss the requirements of the experiment with candidates. Individuals who expressed interest in participating were asked to complete a pre-screening form, a record of informed consent, and a department of motor vehicles form consenting to release of their driving record. All participants were between 18 and 64 years of age and held a valid driver’s license. The driving record for each volunteer was evaluated to eliminate drivers with excessive violations. The criteria for excluding drivers were as follows: (a) more than one violation in the preceding year; (b) more than three recorded violations; and (c) any driving while intoxicated violation. Forty-three individuals were recruited to participate. Of these, five did not complete the drive because the eye tracker could not be calibrated to track their eye movements accurately. Data from an additional seven participants were excluded as the result of equipment failures (e.g., loose camera). In the end, usable data was collected from 31 participants (12 males, M = 46 years; 19 females, M = 47 years). Fourteen participants drove at night and 17 drove during the day. Procedures Data were collected from two participants per day (beginning at approximately 12:45 p.m. and 7:00 p.m.). Data collection began on September 18, 2009, and was completed on October 26, 2009. Pre-Data Collection Activities Participants were greeted by two researchers and asked to complete a fitness to drive questionnaire. This questionnaire focused on drivers’ self-reports of alertness and use of substances that might impair driving (e.g., alcohol). All volunteers appeared fit. Next, the participant and both researchers moved to the eye tracking calibration location and the test vehicle. The calibration procedure took approximately 20 minutes. Calibration of the eye tracking system entailed development of a profile for each participant. This was accomplished by taking multiple photographs of the participant’s face as they slowly rotate their head from side to side. The saved photographs include points on the face for subsequent real-time head and eye tracking. Marked coordinates on the face photographs were edited by the experimenter as needed to improve the real-time face tracking. The procedure also included gaze calibration in which participants gazed at nine points on a wall. These points had been carefully plotted on the wall and correspond to the points in the eye tracking system’s world model. Gaze calibration relates the individual participant’s gaze vectors to known points in the real world. The eye tracking system uses two pulsating infrared sources mounted on the dashboard to create two corneal glints that are used to calculate gaze direction vectors. The glints were captured at 60 Hz. A second set 22 of cameras (scene cameras), fixed on top of the car close to the driver’s viewpoint, were used to produce a video scene of the area ahead. The scene cameras recorded at 25 Hz. A parallax correction algorithm compensated for the distance between the driver’s viewpoint and the scene cameras so that later processing could use the gaze vectors to show where in the forward scene the driver was gazing. If it was not possible to calibrate the eye tracking system to a participant, the participant was dismissed and paid for their time. Causes of calibration failure included reflections from eye glasses, participant height (which put their eyes outside the range of the system), and eyelids that obscure a portion of the pupil. Practice After eye-tracker calibration, a short practice drive was made. Participants were shown a map of the route and written turn-by-turn directions prior to beginning the practice drive. Throughout the drive, verbal directions were provided by a GPS device. During the practice drive, a researcher in the rear seat of the vehicle monitored the accuracy of eye tracking. If the system was tracking poorly, additional calibration was performed. If the calibration could not be improved, the participant was paid for their time and dismissed. Data Collection Participants drove two test routes (referred to as route A and B). Each route required 25 to 30 minutes to complete and included both freeway and arterial segments. Route A was 13 miles long and contained 6 DCZs. Route B was 16 miles long and contained 4 DCZs. Combined, participants drove in a total of 10 DCZs. Similar to the practice drive, participants were shown a map of the route and written turn-by-turn directions. A GPS device provided turn-by-turn guidance during the drive. Roughly one half of the participants drove route A first and the remaining participants began with route B. A 5 minute break followed the completion of the first route. During the drives, a researcher in the front passenger seat assisted the driver when additional route guidance was required. The researcher was also tasked with recording near misses and driver errors if these occurred. The researcher in the rear seat monitored the performance of the eye tracker. If the eye tracker performance became unacceptable (i.e., loss of calibration), then the researcher in the rear asked the participant to park in a safe location so that the eye tracker could be recalibrated. This recalibration typically took a minute or two to accomplish. Debriefing After driving both routes, the participants provided comments regarding their drives. The comments were in reference to the use of a navigation system. No questions were asked about billboards. The participants were given $120.00 in cash for their participation. 23 DATA REDUCTION Eye Tracking Measures The Multiple-Analysis of Psychophysical and Performance Signals (MAPPS™) software was used to reduce the eye tracking data. (36) The software integrates the video output from the scene cameras with the output from the eye tracking software (e.g., gaze vectors). The analysis software provides an interface in which the gaze vectors determined by the eye tracker can be related to areas or objects in the scene camera view of the world. Analysts can indicate regions of interest (ROIs) in the scene camera views and the analysis software then assigns gaze vectors to the ROIs. Figure 9 shows a screen capture from the analysis software in which static ROIs have been identified. These static ROIs slice up the scene camera views into six areas. The software also allows for the construction of dynamic ROIs. These are ROIs that move in the video because of own-vehicle movement (e.g., a sign changes position on the display as it is approached by the driver) or because the object moves over time independent of own-vehicle movement (e.g., pedestrian walking along the road, vehicle entering or exiting the road). Static ROIs need only be entered once for the scenario being analyzed whereas dynamic ROIs need to be entered several times for a given DCZ depending on how the object moves along the video scene; however, not every frame needs to be coded with a dynamic ROI since the software interpolates across frames using the 60-Hz data to compute eye movement statistics. Figure 9. Screen capture showing static ROIs on a scene video output. The following ROIs were defined with the analysis software: Static ROIs These ROIs were entered once into the software for each participant. The static ROIs for the windshield were divided into top and bottom to have more resolution during the coding process. The subsequent analyses in the report combines the top and bottom portion of these ROIs since it appeared that this additional level of resolution was not needed in order to address research questions: • Road ahead: bottom portion (approximately 2/3) of the area of the forward roadway (center camera). 24 • Road ahead top: top portion (approximately 1/3) of the area of the forward roadway (center camera). • Right side of road bottom: bottom portion (approximately 2/3) of the area to the right of the forward roadway (right camera). • Right side of road top: top portion (approximately 1/3) of the area to the right of the forward roadway (right camera). • Left side of road bottom (LSR_B): bottom portion (approximately 2/3) of the area to the left of the forward roadway (left camera). • Left side of road bottom (LSR_T): top portion (approximately 1/3) of the area to the left of the forward roadway (left camera). • Inside vehicle: below the panoramic video scene (outside of the view of the cameras, but eye tracking is still possible). • Top: above the panoramic video scene (outside of the view of the cameras, but eye tracking is still possible). Dynamic ROIs These ROIs are created multiple times within a DCZ for stimuli that move relative to the driver: • Driving-related safety risk: vehicle which posed a potential safety risk to the driver, defined as a car that is/may turn into the driver’s direction of travel at a non-signalized or non-stop-controlled intersection (e.g., a car making a U-turn, a car waiting to turn right, or a car waiting to turn left). These vehicles were actively turning or entering the roadway or appeared to be in a position to enter the roadway. • Target standard billboard: target standard billboard that defines the start and end of the DCZ. • Other standard billboard: standard billboard(s) located in the DCZ, other than the target standard billboard or the target digital billboard. • CEVMS: target digital billboard that defines the start and end of the DCZ. The software determines the gaze intersection for each 60 Hz frame and assigns it to an ROI. In subsequent analyses and discussion, gaze intersections are referred to as gazes. Since ROIs may overlap, the software allows for the specification of priority for each ROI such that the ROI with the highest priority gets the gaze vector intersection assigned to it. For example, an ROI for a CEVMS may also be in the static ROI for the road ahead. 25 The 60 Hz temporal resolution of the eye tracking software does not provide sufficient information to make detailed analysis of saccade characteristics, 1 such as latency or speed. The analysis software uses three parameters in the determination of a fixation: a fixation radius, fixation duration, and a time out. The determination begins with a single-gaze vector intersection. Any subsequent intersection within a specified radius will be considered part of a fixation if the minimum fixation duration criterion is met. The radius parameter used in this study was 2 degrees and the minimum duration was 100 ms. The 2-degree selection was based on the estimated accuracy of the eye tracking system, as recommended by Recarte and Nunes. (37) The 100 ms minimum duration is consistent with many other published studies; however, some investigators use minimums of as little as 60 ms. (37,38) Because of mini-saccades and noise in the eye tracking system, it is possible to have brief excursions outside the 2 degree window for a fixation. In this study, an excursion time outside the 2-degree radius of less than 90 ms was ignored. Once the gaze intersection fell outside the 2-degree radius of a fixation for more than 90 ms, the process of identifying a fixation began anew. Other Measures Driving Behavior Measures During data collection, the front-seat researcher observed the driver’s behavior and the driving environment. The researcher used the following subjective categories in observing the participant’s driving behavior: • Driver Error: signified any error on behalf of the driver in which the researcher felt slightly uncomfortable, but not to a significant degree (e.g., driving on an exit ramp too quickly, turning too quickly). • Near Miss: signified any event in which the researcher felt uncomfortable due to driver response to external sources (e.g., slamming on brakes, swerving). A near miss is the extreme case of a driver error. • Incident: signified any event in the roadway which may have had a potential impact on the attention of the driver and/or the flow of traffic (e.g., crash, emergency vehicle, animal, construction, train). These observations were entered into a notebook computer linked to the research vehicle data collection system. Level of Service Estimates For each participant and each DCZ the analyst estimated the level of service of the road as they reviewed the scene camera video. One location per DCZ was selected (approximately halfway through the DCZ) where the number of vehicles in front of the research vehicle was counted. The procedure entailed (1) counting the number of travel lanes visible in the video, (2) using the 1 During visual scanning, the point of gaze alternates between brief pauses (ocular fixations) and rapid shifts (saccades). 26 skip lines on the road to estimate the approximate distance in front of the vehicle that constituted the analysis zone, and (3) counting the number of vehicles present within the analysis zone. Vehicle density was calculated with the formula: Vehicle Density = [(Number of Vehicles in Analysis Zone)/(Distance of Analysis Zone in ft/5280)]/Number of Lanes. Vehicle density is the number of vehicles per mile per lane. Vehicle Speed The speed of the research vehicle was recorded with GPS and a distance measurement instrument. Vehicle speed was used principally to ensure that the eye tracking data was recorded while the vehicle was in motion. RESULTS Results are presented with respect to the photometric measures of signs, the visual complexity of the DCZs, and the eye tracking measures. Photometric measurements were taken and analyzed to characterize the billboards in the study based on their luminance and contrasts, which are related to how bright the signs are perceived to be by drivers. Photometric Measurements Luminance The mean daytime luminance of both the standard billboards and CEVMS was greater than at night. Nighttime luminance measurements reflect the fact that CEVMS use illuminating LED components while standard billboards are often illuminated from below by metal halide lamps. At night, CEVMS have a greater average luminance than standard billboards. Table 3 presents summary statistics for luminance as a function of time of day for the CEVMS and standard billboards. Contrast The daytime and nighttime Weber contrast ratios for both types of billboards are shown in table 3. Both CEVMS and standard billboards had contrast ratios that were close to zero (the surroundings were about equal in brightness to the signs) during the daytime. On the other hand, at night the CEVMS and standard billboards had positive contrast ratios (the signs were brighter than the surrounding), with the CEVMS having higher contrast than the standard billboards. 27 Table 3. Summary of luminance (cd/m 2 ) and contrast (Weber ratio) measurements. Luminance (cd/m2) Contrast Day Mean St. Dev. Mean St .Dev. CEVMS 2126 798.81 -0.10 0.54 Standard Billboard 2993 2787.22 -0.27 0.84 Night CEVMS 56.00 23.16 73.72 56.92 Standard Billboard 17.80 17.11 36.01 30.93 Visual Complexity The DCZs were characterized by their overall visual complexity or clutter. For each DCZ, five pictures were taken from the driver’s viewpoint at various locations within the DCZ. In Reading, the pictures were taken from 2:00 p.m. to 4:00 p.m. In Richmond, one route was photographed from 11:00 a.m. to noon and the other from 2:30 p.m. to 3:30 p.m. The pictures were taken at the start of the DCZ, quarter of the way through, half of the way through, three quarters of the way through, and at the end of the DCZ. The photographs were analyzed with MATLAB® routines that computed a measure of feature congestion for each image. Figure 10 shows the mean feature congestion measures for each of the DCZ environments. The arterial control condition was shown to have the highest level of clutter as measured by feature congestion. An analysis of variance was performed on the feature congestion measure to determine if the conditions differed significantly from each other. The four conditions with off-premise advertising did not differ significantly with respect to feature congestion; F(3,36) = 1.25, p > 0.05. Based on the feature congestion measure, the results indicate that the four conditions with off-premise advertising were equated with respect to the overall visual complexity of the driving scenes. Figure 10. Mean feature congestion as a function of advertising condition and road type (standard errors for the mean are included in the graph). 28 Effects of Billboards on Gazes to the Road Ahead For each 60 Hz frame, a determination was made as to the direction of the gaze vector. Previous research has shown that gazes do not need to be separated into saccades and fixations before calculating such measures as percent of time or the probability of looking to the road ahead. (39) This analysis examines the degree to which drivers gaze toward the road ahead across the different advertising conditions as a function of road type and time of day. Gazing toward the road ahead is critical for driving, and so the analysis examines the degree to which gazes toward this area are affected by the independent variables (advertising type, type of road, and time of day) and their interactions. Generalized estimating equations (GEE) were used to analyze the probability of a participant gazing at driving-related information. (40,41) The data for these analyses were not normally distributed and included repeated measures. The GEE model is appropriate for these types of data and analyses. Note that for all results included in this report, Wald statistics were the chosen alternative to likelihood ratio statistics because GEE uses quasi-likelihood instead of maximum likelihood. (42) For this analysis, road ahead included the following ROIs (as previously described and displayed in figure 9): road ahead, road ahead top, and driving-related risks. A logistic regression model for repeated measures was generated by using a binomial response distribution and Logit (i.e., log odds) link function. Only two possible outcomes are allowed when selecting a binomial response distribution. Thus, a variable (RoadAhead) was created to classify a participant’s gaze behavior. If the participant gazed toward the road ahead, road ahead top, or driving-related risks, then the value of RoadAhead was set to one. If the participant gazed at any other object in the panoramic scene, then the value of RoadAhead was set to zero. Logistic regression typically models the probability of a success. In the current analysis, a success would be a gaze to road ahead information (RoadAhead = 1) and a failure would be a gaze toward non- road ahead information (RoadAhead = 0). The resultant value was the probability of a participant gazing at road-ahead information. Time of day (day or night), road type (freeway or arterial), advertising condition (CEVMS, standard billboard, or control), and all corresponding second-order interactions were explanatory variables in the logistic regression model. The interaction of advertising condition by road type was statistically significant, χ 2 (2) = 6.3, p = 0.043. Table 4 shows the corresponding probabilities for gazing at the road ahead as a function of advertising condition and road type. Table 4. The probability of gazing at the road ahead as a function of advertising condition and road type. Advertising Condition Arterial Freeway Control 0.92 0.86 CEVMS 0.82 0.73 Standard 0.80 0.77 Follow-up analyses for the interaction used Tukey-Kramer adjustments with an alpha level of 0.05. The arterial control condition had the greatest probability of looking at the road ahead (M = 0.92). This probability differed significantly from the remaining five probabilities. On 29 arterials, the probability of gazing at the road ahead did not differ between the CEVMS (M = 0.82) and the standard billboard (M = 0.80) DCZs. In contrast, there was a significant difference in this probability on freeways, where standard billboard DCZs yielded a higher probability (M = 0.77) than CEVMS DCZs (M = 0.73). The probability of gazing at the road ahead was also significantly higher in the freeway control DCZ (M = 0.86) than in either of the corresponding freeway off-premise advertising DCZs. The probability of gazing at road-ahead information in arterial CEVMS DCZs was not statistically different from the same probability in the freeway control DCZ. Additional descriptive statistics were computed to determine the probability of gazing at the various ROIs that were defined in the panoramic scene. Some of the ROIs depicted in figure 9 were combined in the following fashion for ease of analysis: • Road ahead, road ahead top, and driving-related risks combined to form road ahead. • Left side of road bottom and left side of road top combined to form left side of vehicle. • Right side of road bottom and right side of road top combined to form right side of vehicle. • Inside vehicle and top combined to form participant vehicle. Table 5 presents the probability of gazing at the different ROIs. Table 5. Probability of gazing at ROIs for the three advertising conditions on arterials and freeways. Road Type ROI CEVMS Standard Billboard Control Arterial CEVMS 0.07 N/A N/A Left Side of Vehicle 0.06 0.06 0.02 Road ahead 0.82 0.80 0.92 Right Side of Vehicle 0.03 0.06 0.04 Standard Billboard N/A 0.03 N/A Participant Vehicle 0.03 0.05 0.02 Freeway CEVMS 0.05 N/A N/A Left Side of Vehicle 0.08 0.07 0.04 Road ahead 0.73 0.77 0.86 Right Side of Vehicle 0.09 0.02 0.05 Standard Billboard 0.02* 0.09 N/A Participant Vehicle 0.04 0.05 0.05 * The CEVMS DCZs on freeways each contained one visible standard billboard. The probability of gazing away from the forward roadway ranged from 0.08 to 0.27. In particular, the probability of gazing toward a CEVMS was greater on arterials (M = 0.07) than on freeways (M = 0.05). In contrast, the probability of gazing toward a target standard billboard was greater on freeways (M = 0.09) than on arterials (M = 0.03). 30 Fixations to CEVMS and Standard Billboards About 2.4 percent of the fixations were to CEVMS. The mean fixation duration to a CEVMS was 388 ms and the maximum duration was 1,251 ms. Figure 11 shows the distribution of fixation durations to CEVMS during the day and night. In the daytime, the mean fixation duration to a CEVMS was 389 ms and at night it was 387 ms. Figure 12 shows the distribution of fixation durations to standard billboards. Approximately 2.4 percent of fixations were to standard billboards. The mean fixation duration to standard billboards was 341 ms during the daytime and 370 ms at night. The maximum fixation duration to standard billboards was 1,284 ms (which occurred at night). For comparison purposes, figure 13 shows the distribution of fixation durations to the road ahead (i.e., top and bottom road ahead ROIs) during the day and night. In the daytime, the mean fixation duration to the road ahead was 365 ms and at night it was 390 ms. Figure 11. Distribution of fixation duration for CEVMS in the daytime and nighttime. 31 Figure 12. Distribution of fixation duration for standard billboards in the daytime and nighttime. Figure 13. Distribution of fixation duration for road ahead (i.e., top and bottom road ahead ROIs) in the daytime and nighttime. 32 Dwell times on CEVMS and standard billboards were also examined. Dwell time is the duration of back-to-back fixations to the same ROI. (43,44) The dwell times represent the cumulative time for the back-to-back fixations. Whereas there may be no long, single fixation to a billboard, there might still be multiple fixations that yield long dwell times. There were a total of 25 separate instances of multiple fixations to CEVMS with a mean of 2.4 fixations (minimum of 2 and maximum of 5). The 25 dwell times came from 15 different participants distributed across four different CEVMS. The mean duration of these dwell times was 994 ms (minimum of 418 ms and maximum of 1,467 ms). For standard billboards, there were a total of 17 separate dwell times with a mean of 3.47 sequential fixations (minimum of 2 fixations and maximum of 8 fixations). The 17 dwell times came from 11 different participants distributed across 4 different standard billboards. The mean duration of these multiple fixations was 1,172 ms (minimum of 418 ms and maximum of 3,319 ms). There were three dwell-time durations that were greater than 2,000 ms. These are described in more detail below. In some cases several dwell times came from the same participant. In order to compute a statistic on the difference between dwell times for CEVMS and standard billboards, average dwell times were computed per participant for the CEVMS and standard billboard conditions. These average values were used in a t-test assuming unequal variances. The difference in average dwell time between CEVMS (M = 981 ms) and standard billboards (M= 1,386 ms) was not statistically significant, t(12) = -1.40, p > .05. Figure 14 through figure 23 show heat maps for the dwell-time durations to the standard billboards that were greater than 2,000 ms. These heat maps are snapshots from the DCZ and attempt to convey in two dimensions the pattern of gazes that took place in a three dimensional world. The heat maps are set to look back approximately one to two seconds and integrate over time where the participant was gazing in the scene camera video. The green color in the heat map indicates the concentration of gaze over the past one to two seconds. The blue line indicates the gaze trail over the past one to two seconds. Figure 14 through figure 16 are for a DCZ on an arterial at night. The standard billboard was on the right side of the road (indicated by a pink rectangle). There were eight fixations to this billboard, and the single fixations were between 200 to 384 ms in duration. The dwell time for this billboard was 2,019 ms. At the start of the DCZ (see figure 14), the driver was directing his/her gaze to the forward roadway. Approaching the standard billboard, the driver began to fixate on the billboard. However, the billboard was still relatively close to the road ahead ROI. 33 Figure 14. Heat map for the start of a DCZ for a standard billboard at night on an arterial. Figure 15. Heat map for the middle of a DCZ for a standard billboard at night on an arterial. Figure 16. Heat map near the end of a DCZ for a standard billboard at night on an arterial. Figure 17 through figure 19 are for a DCZ on a freeway at night. The standard billboard was on the right side of the road (indicated by a green rectangle). There were six consecutive fixations to this billboard, and the single fixations were between 200 and 801 ms in duration. The dwell time for this billboard was 2,753 ms. At the start of the DCZ (see figure 17), the driver was directing his/her gaze to a freeway guide sign in the road ahead and the standard billboard was to the left of the freeway guide sign. As the driver approached the standard billboard, his/her gaze was directed toward the billboard. The billboard was relatively close to the top and bottom road ahead ROIs. Near the end of the DCZ (see figure 19), the billboard was accurately portrayed as being on the right side of the road. 34 Figure 17. Heat map for start of a DCZ for a standard billboard at night on a freeway. Figure 18. Heat map for middle of a DCZ for a standard billboard at night on a freeway. Figure 19. Heat map near the end of a DCZ for a standard billboard at night on a freeway. Figure 20 through figure 23 are for a DCZ on a freeway during the day. The standard billboard was on the right side of the road (indicated by a pink rectangle). This is the same DCZ that was discussed in figure 17 through figure 19. There were six consecutive fixations to this billboard, and the single fixations were between 217 and 767 ms in duration. The dwell time for this billboard was 3,319 ms. At the start of the DCZ (see figure 20), the driver was principally directing his/her gaze to the road ahead. Figure 21 and figure 22 show the location along the DCZ where gaze was directed toward the standard billboard. The billboard was relatively close to the top and bottom road-ahead ROIs. As the driver passed the standard billboard, his/her gaze returned to the road ahead (see figure 23). 35 Figure 20. Heat map for the start of a DCZ for a standard billboard in the daytime on a freeway. Figure 21. Heat map near the middle of a DCZ for a standard billboard in the daytime on a freeway. Figure 22. Heat map near the end of DCZ for standard billboard in the daytime on a freeway. Figure 23. Heat map at the end of DCZ for standard billboard in the daytime on a freeway. 36 Comparison of Gazes to CEVMS and Standard Billboards The GEE were used to analyze whether a participant gazed more toward CEVMS than toward standard billboards, given that the participant was gazing at off-premise advertising. With this analysis method, a logistic regression model for repeated measures was generated by using a binomial response distribution and Logit link function. First, the data was partitioned to include only those instances when a participant was gazing toward off-premise advertising (either to a CEVMS or to a standard billboard); all other gaze behavior was excluded from the input data set. Only two possible outcomes are allowed when selecting a binomial response distribution. Thus, a variable (SBB_CEVMS) was created to classify a participant’s gaze behavior. If the participant gazed toward a CEVMS, the value of SBB_CEVMS was set to one. If the participant gazed toward a standard billboard, then the value of SBB_CEVMS was set to zero. Logistic regression typically models the probability of a success. In the current analysis, a success would be a gaze to a CEVMS (SBB_CEVMS = 1) and a failure would be a gaze to a standard billboard (SBB_CEVMS = 0). 2 A success probability greater than 0.5 indicates there were more successes than failures in the sample. Therefore, if the sample probability of the response variable (i.e., SBB_CEVMS) was greater than 0.5, this would show that participants gazed more toward CEVMS than toward standard billboards when the participants gazed at off- premise advertising. In contrast, if the sample probability of the response variable was less than 0.5, then participants showed a preference to gaze more toward standard billboards than toward CEVMS when directing gazes to off-premise advertising. Time of day (i.e., day or night), road type (i.e., freeway or arterial), and the corresponding interaction were explanatory variables in the logistic regression model. Road type was the only predictor to have a significant effect, χ 2 (1) = 13.17, p < 0.001. On arterials, participants gazed more toward CEVMS than toward standard billboards (M = 0.63). In contrast, participants gazed more toward standard billboards than toward CEVMS when driving on freeways (M = 0.33). Observation of Driver Behavior No near misses or driver errors were observed in Reading. Level of Service The mean vehicle densities were converted to level of service as shown in table 6. (45) As expected, less congestion occurred at night than in the day. In general, there was traffic during the data collection runs. Review of the scene camera data verified that all eye tracking data within the DCZs were recorded while the vehicle was in motion. 2 Success and failure are not used to reflect the merits of either type of sign, but only for statistical purposes. 37 Table 6. Level of service as a function of advertising type, road type, and time of day. Arterial Freeway Day Night Day Night Control B A C B CEVMS C A B A Standard A A B A DISCUSSION OF READING RESULTS Overall the probability of gazing at the road ahead was high and similar in magnitude to what has been found in other field studies addressing billboards. (11,9,12) For the DCZs on freeways, CEVMS showed a lower proportion of gazes to the road ahead than the standard billboard condition, and both off-premise advertising conditions had lower probability of gazes to the road ahead than the control. On the other hand, on the arterials, the CEVMS and standard billboard conditions did not differ from each other but were significantly different from their respective control condition. Though the CEVMS condition on the freeway had the lowest proportion of gazes to the road ahead, in this condition there was a lower proportion of gazes to CEVMS as compared to the arterials (see table 5 for the trade-off of gazes to the different ROIs). A greater proportion of gazes to other ROIs (left side of the road, right side of the road, and participant vehicle) contributed to the decrease in proportion of gazes to the road ahead. Also, for the CEVMS on freeways, there were a few gazes to a standard billboard located in the same DCZ and there were more gazes distributed to the left and right side of the road than in standard billboard and control conditions. The gazes to ROIs other than CEVMS contributed to the lower probability of gazes to the road ahead in this condition. The control condition on the arterial had buildings along the sides of the road and generally presented a visually cluttered area. As was presented earlier, the feature congestion measure computed on a series of photographs from each DCZ showed a significantly higher feature congestion score for the control condition on arterials as compared to all of the other DCZs. Nevertheless, the highest probability for gazing at the road ahead was seen in the control condition on the arterial. The area with the highest feature congestion, especially on the sides of the road, had the highest probability for drivers looking at the road ahead. Bottom-up or stimulus driven measures of salience or visual clutter have been useful in predicting visual search and the effects of visual salience in laboratory tasks. (34,46) These measures of salience basically consider the stimulus characteristics (e.g., size, color, brightness) independent of the requirements of the task or plans that an individual may have. Models of visual salience may predict that buildings and other prominent features on the side of the road may be visually salient objects and thus would attract a driver’s attention. (47) Figure 24 shows an example of a roadway photograph that was analyzed with the Salience Toolbox based on the Itti et al. implementation of a saliency based model of bottom-up attention. (48,49) The numbered circles in figure 24 are the first through fifth salient areas selected by the software. Based on this software, the most salient areas in the photographs are the buildings on the sides of the road where the road ahead (and a car) is the fifth selected salient area. 38 Figure 24. Example of identified salient areas in a road scene based on bottom-up analysis. It appears that in the present study participants principally kept their eyes on the road even in the presence of visual clutter on the sides of the road, which supports the hypothesis that drivers tend to look toward information relevant to the task at hand. (50,26,22) In the case of the driving task, visual clutter may be more of an issue with respect to crowding that may affect the driver’s ability to detect visual information in the periphery. (51) Crowding is generally defined as the negative effect of nearby objects or features on visual discrimination of a target. (52) Crowding impairs the ability to recognize objects in clutter and principally affects perception in peripheral vision. However, crowing effects were not analyzed in the present study. Stimulus salience, clutter, and the nature of the task at hand interact in visual perception. For tasks such as driving, the task demands tend to outweigh stimulus salience when it comes to gaze control. Clutter may be more of an issue with the detection and recognition of objects in peripheral vision (e.g., detecting a sign on the side of the road) that are surrounded by other stimuli that result in a crowding effect. The mean fixation durations to CEVMS, standard billboards, and the road ahead were found to be very similar. Also, there were no long fixations (greater than 2,000 ms) to CEVMS or standard billboards. The examination of multiple sequential fixations to CEVMS yielded average dwell times that were less than 1,000 ms. However, when examining the tails of the distribution, there were three dwell times to standard billboards that were in excess of 2,000 ms (the three dwell times came from three different participants to two different billboards). These three standard billboards were dwelled upon when they were near the road ahead area but drivers quit gazing at the signs as they neared them and the signs were no longer near the forward field of view. Though there were three dwell times for standard billboards greater than 2,000 ms, the difference in average dwell times for CEVMS and standard billboards was not significant. Using a gaze duration of 2,000 ms away from the road ahead as a criterion indicative of increased risk has been developed principally as it relates to looking inside the vehicle to in- vehicle information systems and other devices (e.g., for texting) where the driver is indeed looking completely away from the road ahead. (14,53,54) The fixations to the standard billboards in the present case showed a long dwell time for a billboard. However, unlike gazing or fixating inside the vehicle, the driver’s gaze was within the forward roadway where peripheral vision could be used to monitor for hazards and for vehicle control. Peripheral vision has been shown to be important for lane keeping, visual search orienting, and monitoring of surrounding objects. (55,56) 39 The results showed that drivers were more likely to gaze at CEVMS on arterials and at standard billboards on freeways. Though every attempt was made to select CEVMS and standard billboard DCZs that were equated on important parameters (e.g., which side of the road the sign was located on, type of road, level of visual clutter), the CEVMS DCZs on freeways had a greater setback from the road (133 ft for both CEVMS) than the standard billboards (10 and 35 ft). Signs with greater setback from the road would in a sense move out of the forward view (road ahead) more quickly than signs that are closer to the road. The CEVMS and standard billboards on the arterials were more closely matched with respect to setback from the road (12 and 43 ft for CEVMS and 20 and 40 ft for standard billboards). The differences in setback from the road for CEVMS and standard billboards may also account for differences in dwell times to these two types of billboards. However, on arterials where the CEVMS and standard billboards were more closely matched there was only one long dwell time (greater than 2,000 ms) and it was to a standard billboard at night. 40 RICHMOND The objectives of the second study were the same as those in the first study, and the design of the Richmond data collection effort was very similar to that employed in Reading. This study was conducted to replicate as closely as possible the design of Reading in a different driving environment. The independent variables included the type of DCZ (CEVMS, standard billboard, or no off-premise advertising), time of day (day or night) and road type (freeway or arterial). As with Reading, the time of day was a between-subjects variable and the other variables were within subjects. METHOD Selection of DCZ Limits Selection of the DCZ limits procedure was the same as that employed in Reading. Advertising Type Three DCZ types (similar to those used in Reading) were used in Richmond: • CEVMS. DCZs contained one target CEVMS. • Standard billboard. DCZs contained one target standard billboard. • Control conditions. DCZs did not contain any off-premise advertising. There were an equal number of CEVMS and standard billboard DCZs on freeways and arterials. Also, there two DCZ that did not contain off-premise advertising with one located on a freeway and the other on an arterial. Table 7 is an inventory of the target employed in this second study. Table 7. Inventory of target billboards in Richmond with relevant parameters. DCZ Advertising Type Copy Dimensions (ft) Side of Road Setback from Road (ft) Other Standard Billboards Approach Length (ft) Roadway Type 5 CONTROL N/A N/A N/A N/A 710 Arterial 3 CONTROL N/A N/A N/A N/A 845 Freeway 9 CEVMS 14'0" x 28'0" L 37 0 696 Arterial 13 CEVMS 14'0" x 28'0" R 37 0 602 Arterial 2 CEVMS 12'5" x 40'0" R 91 0 297 Freeway 8 CEVMS 11'0 x 23'0" L 71 0 321 Freeway 10 Standard 14'0" x 48'0" L 79 1 857 Arterial 12 Standard 10'6" x 45'3" R 79 2 651 Arterial 1 Standard 14'0" x 48'0" L 87 0 997 Freeway 7 Standard 14'0" x 48'0" R 88 0 816 Freeway * N/A indicates that there were no off-premise advertising in these areas and these values are undefined. 41 Figure 25 through figure 30 below represent various pairings of DCZ type and road type. Target off-premise billboards are indicated by red rectangles. Figure 25. Example of a CEVMS DCZ on a freeway. Figure 26. Example of CEVMS DCZ an arterial. Figure 27. Example of a standard billboard DCZ on a freeway. 42 Figure 28. Example of a standard billboard DCZ on an arterial. Figure 29. Example of a control DCZ on a freeway. Figure 30. Example of a control DCZ on an arterial. Photometric Measurement of Signs The methods and procedures for the photometric measures were the same as for Reading. Visual Complexity The methods and procedures for visual complexity measurement were the same as for Reading. 43 Participants A total of 41 participants were recruited for the study. Of these, 6 participants did not complete data collection because of an inability to properly calibrate with the eye tracking system, and 11 were excluded because of equipment failures. A total of 24 participants (13 male, M = 28 years; 11 female, M = 25 years) successfully completed the drive. Fourteen people participated during the day and 10 participated at night. Procedures Research participants were recruited locally by means of visits to public libraries, student unions, community centers, etc. A large number of the participants were recruited from a nearby university, resulting in a lower mean participant age than in Reading. Participant Testing Two people participated each day. One person participated during the day beginning at approximately 12:45 p.m. The second participated at night beginning at around 7:00 p.m. Data collection ran from November 20, 2009, through April 23, 2010. There were several long gaps in the data collection schedule due to holidays and inclement weather. Pre-Data Collection Activities This was the same as in Reading. Practice Drive Except for location, this was the same as in Reading. Data Collection The procedure was much the same as in Reading. On average, each test route required approximately 30 to 35 minutes to complete. As in Reading, the routes included a variety of freeway and arterial driving segments. One route was 15 miles long and contained two target CEVMS, two target standard billboards, and two DCZs with no off-premise advertising. The second route was 20 miles long and had two target CEVMS and two target standard billboards. The data collection drives in this second study were longer than those in Reading. The eye tracking system had problems dealing with the large files that resulted. To mitigate this technical difficulty, participants were asked to pull over in a safe location during the middle of each data collection drive so that new data files could be initiated. Upon completion of the data collection, the participant was instructed to return to the designated meeting location for debriefing. Debriefing This was the same as in Reading. 44 DATA REDUCTION Eye Tracking Measures The approach and procedures were the same as used in Reading. Other Measures The approach and procedures were the same as used in Reading. RESULTS Photometric Measurement of Signs The photometric measurements were performed using the same equipment and procedures that were employed in Reading with a few minor changes. Photometric measurements were taken during the day and at night. Measurements of the standard billboards were taken at an average distance of 284 ft, with maximum and minimum distances of 570 ft and 43 ft, respectively. The average distance of measurements for the CEVMS was 479 ft, with maximum and minimum distances of 972 ft and 220 ft, respectively. Again, the distances employed were significantly affected by the requirement to find a safe location on the road from which to take the measurements. Luminance The mean luminance of CEVMS and standard billboards, during daytime and nighttime are shown below in table 8. The results here are similar to those for Reading. Contrast The daytime and nighttime Weber contrast ratios for both types of billboards are shown in table 8. During the day, the contrast ratios of both CEVMS and standard billboards were close to zero (the surroundings were about equal in brightness to the signs). At night, the CEVMS and standard billboards had positive contrast ratios. Similar to Reading, the CEVMS showed a higher contrast ratio than the standard billboards at night. Table 8. Summary of luminance (cd/m 2 ) and contrast (Weber ratio) measurements. Luminance (cd/m2) Contrast Day Mean St. Dev. Mean St. Dev. CEVMS 2134 798.70 -0.20 0.53 Standard Billboard 3063 2730.92 0.03 0.32 Night CEVMS 56.44 16.61 69.70 59.18 Standard Billboard 8.00 5.10 6.56 3.99 45 Visual Complexity As with Reading, the feature congestion measure was used to estimate the level of visual complexity/clutter in the DCZs. The analysis procedures were the same as for Reading. Figure 31 shows the mean feature congestion measures for each of the advertising types (standard errors are included in the figure). Unlike the results for Reading, the selected off- premise advertising DCZs for Richmond differed in terms of mean feature congestion; F(3, 36) = 3.95, p = 0.016. Follow up t-tests with an alpha of 0.05 showed that the CEVMS DCZs on arterials had significantly lower feature congestion than all of the other off-premise advertising conditions. None of the remaining DCZs with off-premise advertising differed from each other. The selection of DCZs for the conditions with off-premise advertising took into account the type of road, the side of the road the target billboard was placed, and the perceived level of visual clutter. Based on the feature congestion measure, these results indicated that the conditions with off-premise advertising were not equated with respect to level of visual clutter. Figure 31. Mean feature congestion as a function of advertising condition and road type. Effects of Billboards on Gazes to the Road Ahead As was done for the data from Reading, GEE were used to analyze the probability of a participant gazing at the road ahead. A logistic regression model for repeated measures was generated by using a binomial response distribution and Logit link function. The resultant value was the probability of a participant gazing at the road ahead (as previously defined). Time of day (day or night), road type (freeway or arterial), advertising type (CEVMS, standard billboard, or control), and all corresponding second-order interactions were explanatory variables in the logistic regression model. The interaction of advertising type by road type was statistically significant, χ 2 (2) = 14.19, p < 0.001. Table 9 shows the corresponding probability of gazing at the road ahead as a function of advertising condition and road type. 46 Table 9. The probability of gazing at the road ahead as a function of advertising condition and road type. Advertising Condition Arterial Freeway Control 0.78 0.92 CEVMS 0.76 0.82 Standard 0.81 0.85 Follow-up analyses for the interaction used Tukey-Kramer adjustments with an alpha level of 0.05. The freeway control had the greatest probability of gazing at the road ahead (M = 0.92). This probability differed significantly from the remaining five probabilities. On arterials, there were no significant differences among the probabilities of gazing at the road ahead among the three advertising conditions. On freeways, there was no significant difference between the probability associated with CEVMS DCZs and the probability associated with standard billboard DCZs. Additional descriptive statistics were computed for the three advertising types to determine the probability of gazing at the ROIs that were defined in the panoramic scene. As was done with the data from Reading, some of the ROIs were combined for ease of analysis. Table 10 presents the probability of gazing at the different ROIs. Table 10. Probability of gazing at ROIs for the three advertising conditions on arterials and freeways. Road Type ROI CEVMS Standard Billboard Control Arterial CEVMS 0.06 N/A N/A Left Side of Vehicle 0.03 0.05 0.04 Road ahead 0.76 0.81 0.78 Right Side of Vehicle 0.07 0.06 0.09 Standard Billboard N/A 0.02 N/A Participant Vehicle 0.07 0.06 0.09 Freeway CEVMS 0.05 N/A N/A Left Side of Vehicle 0.03 0.01 0.01 Road ahead 0.82 0.85 0.92 Right Side of Vehicle 0.04 0.04 0.03 Standard Billboard N/A 0.04 N/A Participant Vehicle 0.06 0.06 0.05 The probability of gazing away from the forward roadway ranged from 0.08 to 0.24. In particular, the probability of gazing toward a CEVMS was slightly greater on arterials (M = 0.06) than on freeways (M = 0.05). In contrast, the probability of gazing toward a standard billboard was greater on freeways (M = 0.04) than on arterials (M = 0.02). In both situations, the probability of gazing at the road ahead was greatest on freeways. 47 Fixations to CEVMS and Standard Billboards About 2.5 percent of the fixations were to CEVMS. The mean fixation duration to a CEVMS was 371 ms and the maximum fixation duration was 1,335 ms. Figure 32 shows the distribution of fixation durations to CEVMS during the day and at night. In the daytime, the mean fixation duration to a CEVMS was 440 ms and at night it was 333 ms. Approximately 1.5 percent of the fixations were to standard billboards. The mean fixation duration to standard billboards was 318 ms and the maximum fixation duration was 801 ms. Figure 33 shows the distribution of fixation durations for standard billboards. The mean fixation duration to a standard billboard was 313 ms and 325 ms during the day and night, respectively. For comparison purposes, figure 34 shows the distribution of fixation durations to the road ahead during the day and night. In the daytime, the mean fixation duration to the road ahead was 378 ms and at night it was 358 ms. Figure 32. Fixation duration for CEVMS in the day and at night. 48 Figure 33. Fixation duration for standard billboards in the day and at night. Figure 34. Fixation duration for the road ahead in the day and at night. 49 As was done with the data for Reading, the record of fixations was examined to determine dwell times to CEVMS and standard billboards. There were a total of 21 separate dwell times to CEVMS with a mean of 2.86 sequential fixations (minimum of 2 fixations and maximum of 6 fixations). The 21 dwell times came from 12 different participants and four different CEVMS. The mean dwell time duration to the CEVMS was 1,039 ms (minimum of 500 ms and maximum of 2,720 ms). There was one dwell time greater than 2,000 ms to CEVMS. To the standard billboards there were 13 separate dwell times with a mean of 2.31 sequential fixations (minimum of 2 fixations and maximum of 3 fixations). The 13 dwell times came from 11 different participants and four different standard billboards. The mean dwell time duration to the standard billboards was 687 ms (minimum of 450 ms and maximum of 1,152 ms). There were no dwell times greater than 2,000 ms to standard billboards. In some cases several dwell times came from the same participant. To compute a statistic on the difference between dwell times for CEVMS and standard billboards, average dwell times were computed per participant for the CEVMS and standard billboard conditions. These average values were used in a t-test assuming unequal variances. The difference in average dwell time between CEVMS (M = 1,096 ms) and standard billboards (M= 674 ms) was statistically significant, t(14) = 2.23, p = .043. Figure 35 through figure 37 show heat maps for the dwell-time durations to the CEVMS that were greater than 2,000 ms. The DCZ was on a freeway during the daytime. The CEVMS is located on the left side of the road (indicated by an orange rectangle). There were three fixations to this billboard, and the single fixations were between 651 ms and 1,335 ms. The dwell time for this billboard was 2,270 ms. Figure 35 shows the first fixation toward the CEVMS. There are no vehicles near the participant in his/her respective travel lane or adjacent lanes. In this situation, the billboard is relatively close to the road ahead ROI. Figure 36 shows a heat map later in the DCZ where the driver continues to look at the CEVMS. The heat map does not overlay the CEVMS in the picture since the heat map has integrated over time where the driver was gazing. The CEVMS has moved out of the area because of the vehicle moving down the road. However, visual inspection of the video and eye tracking statistics showed that the driver was fixating on the CEVMS. Figure 37 shows the end of the sequential fixations to the CEVMS. The driver returns to gaze directly in front of the vehicle. Once the CEVMS was out of the forward field of view, the driver quit looking at the billboard. Figure 35. Heat map for first fixation to CEVMS with long dwell time. 50 Figure 36. Heat map for later fixations to CEVMS with long dwell time. Figure 37. Heat map at end of fixations to CEVMS with long dwell time. Comparison of Gazes to CEVMS and Standard Billboards As was done for the data from Reading, GEE were used to analyze whether a participant gazed more toward CEVMS than toward standard billboards, given that the participant was looking at off-premise advertising. Recall that a sample probability greater than 0.5 indicated that participants gazed more toward CEVMS than standard billboards when the participants gazed at off-premise advertising. In contrast, if the sample probability was less than 0.5, participants showed a preference to gaze more toward standard billboards than CEVMS when directing visual attention to off-premise advertising. Time of day (i.e., day or night), road type (i.e., freeway or arterial), and the corresponding interaction were explanatory variables in the logistic regression model. Time of day had a significant effect on participant gazes toward off-premise advertising, χ 2 (1) = 4.46, p = 0.035. Participants showed a preference to gaze more toward CEVMS than toward standard billboards during both times of day. During the day the preference was only slight (M = 0.52), but at night the preference was more pronounced (M = 0.71). Road type was also a significant predictor of where participants directed their gazes at off-premise advertising, χ 2 (1) = 3.96, p = 0.047. Participants gazed more toward CEVMS than toward standard billboards while driving on both types of roadways. However, driving on freeways yielded a slight preference for CEVMS over standard billboards (M = 0.55), but driving on arterials resulted in a larger preference in favor of CEVMS (M = 0.68). 51 Observation of Driver Behavior No near misses or driver errors occurred. Level of Service Table 11 shows the level of service as a function of advertising type, type of road, and time of day. As expected, there was less congestion during the nighttime runs than in the daytime. In general, there was traffic during the data collection runs; however, the eye tracking data were recorded while the vehicles were in motion. Table 11. Estimated level of service as a function of advertising condition, road type, and time of day. Arterial Freeway Day Night Day Night Control B A C B CEVMS B A B A Standard C A C C DISCUSSION OF RICHMOND RESULTS Overall the probability of looking at the forward roadway was high across all conditions and consistent with the findings from Reading and previous related research. (11,9,12) In this second study the CEVMS and standard billboard conditions did not differ from each other. For the DCZs on arterials there were no significant differences among the control, CEVMS, and standard billboard conditions. On the other hand, while the CEVMS and standard billboard conditions on the freeways did not differ from each other, they were significantly different from their respective control conditions. The control condition on the freeway principally had trees along the sides of the road and the signs that were present were freeway signs located in the road ahead ROI. Measures such as feature congestion rated the three DCZs on freeways as not being statistically different from each other. These types of measures have been useful in predicting visual search and the effects of visual salience in laboratory tasks. (34) Models of visual salience may predict that, at least during the daytime, trees on the side of the road may be visually salient objects that would attract a driver’s attention. (47) However, it appears that in the present study, participants principally kept their eyes on the road ahead. The mean fixations to CEVMS, standard billboards, and the road ahead were found to be similar in magnitude with no long fixations. Examination of dwell times showed that there was one long dwell time for a CEVMS greater than 2,000 ms and it occurred in the daytime on a sign located on the left side of the road on a freeway DCZ. Furthermore, when averaging among participants the mean dwell time for CEVMS was significantly longer than to standard billboards, but still under 2,000 ms. For the dwell time greater than 2,000 ms, examination of the scene camera video and eye tracking heat maps showed that the driver was initially looking toward the forward roadway and made a first fixation to the sign. Three fixations were made to the sign and then the 52 driver started looking back to the road ahead as the sign moved out of the forward field of view. On the video there were no vehicles near the subject driver’s own lane or in adjacent lanes. Only the central 2 degrees of vision, foveal vision, provide resolution sharp enough for reading or recognizing fine detail. (57) However, useful information for reading can be extracted from parafoveal vision, which encompasses the central 10 degrees of vision. (57) More recent research on scene gist recognition 3 has shown that peripheral vision (beyond parafoveal vision) is more useful than central vision for recognizing the gist of a scene. (58) Scene gist recognition is a critically important early stage of scene perception, and influences more complex cognitive processes such as directing attention within a scene and facilitating object recognition, both of which are important in obtaining information while driving. The results of this study do show one duration of eyes off the forward roadway greater than 2,000 ms, the duration at which Klauer et al. observed near-crash/crash risk at more than twice those of normal, baseline driving. (14,53) When looking at the tails of the fixation distributions, few fixations were greater than 1,000 ms, with the longest fixation being equal to 1,335 ms. (53,54) The one long dwell time on a CEVMS that was observed was a rare event in this study, and review of the video and eye tracking data suggests that the driver was effectively managing acquisition of visual information while driving and fixated on the advertising. However, additional work needs to be done to derive criteria for gazing or fixating away from the forward road view where the road scene is still visible in peripheral vision. The results showed that drivers are more likely to look at CEVMS than standard billboards during the nighttime across the conditions tested (at night the average probability of gazing at CEVMS was M= 0.71). CEVMS do have greater luminance than standard billboards at night and also have higher contrast. The CEVMS have the capability of being lit up so that they would appear as very bright signs to drivers (for example, up to about10,000 cd/m 2 for a white square on the sign.). However, our measurements of these signs showed an average luminance of about 56 cd/m 2 . These signs would be conspicuous in a nighttime driving environment but significantly less so than other light sources such as vehicle headlights. Drivers were also more likely to look at CEVMS than standard billboards on both arterials and freeways, with a higher probability of gazes on arterials. In this second study, CEVMS and standard billboards were more nearly equated with respect to setback from the road. Gazes to the road ahead were not significantly different between CEVMS and standard billboard DCZs across conditions and the proportion of gazes to the road ahead were consistent with previous research. One long dwell time for a CEVMS was observed in this study; however, it occurred in the daytime where the luminance and contrast (affecting the perceived brightness) of these signs are similar to those for standard billboards. 3 “Scene gist recognition” refers to the element of human cognition that enables us to determine the meaning of a scene and categorize it by type (e.g., a beach, an office) almost immediately upon seeing it. 53 GENERAL DISCUSSION This study was conducted to investigate the effect of CEVMS on driver visual behavior in a roadway driving environment. An instrumented vehicle with an eye tracking system was used. Roads containing CEVMS, standard billboards, and control areas with no off-premise advertising were selected. The CEVMS and standard billboards were measured with respect to luminance, location, size, and other relevant variables to characterize these visual stimuli. Unlike previous studies on digital billboards, the present study examined CEVMS as deployed in two United States cities and did not contain dynamic video or other dynamic elements. The CEVMS changed content approximately every 8 to 10 seconds, consistent within the limits provided by FHWA guidance. (2) In addition, the eye tracking system used had nearly a 2-degree level of resolution that provided significantly more accuracy in determining what objects the drivers were gazing or fixating on as compared to some previous field studies examining CEVMS. CONCLUSIONS Do CEVMS attract drivers’ attention away from the forward roadway and other driving relevant stimuli? Overall, the probability of looking at the road ahead was high across all conditions. In Reading, the CEVMS condition had a lower proportion of gazes to the road ahead than the standard billboard condition on the freeways. Both of the off-premise advertising conditions had a lower proportion of gazes to the road ahead than the control condition on the freeway. The lower proportion of gazes to the road ahead can be attributed to the overall distribution of gazes away from the road ahead and not just to the CEVMS. On the other hand, for the arterials the CEVMS and standard billboard conditions did not differ from each other, but both had a lower proportion of gazes to the road ahead compared to the control. In Richmond there were no differences among the three advertising conditions on the arterials. However, for the freeways the CEVMS and standard billboard conditions did not differ from each other but had a lower proportion of gazes to the road ahead than the control. The control conditions differed across studies. In Reading, the control condition on arterials showed 92 percent for gazing at the road ahead while on the freeway it was 86 percent. On the other hand, in Richmond the control condition for arterials was 78 percent and for the freeway it was 92 percent. The control conditions on the freeway differed across the two studies. In Reading there were businesses off to the side of the road; whereas in Richmond the sides of the road were mostly covered with trees. The control conditions on the arterials also differed across cities in that both contained businesses and on-premise advertising; however, in Reading arterials had four lanes and in Richmond arterials had six lanes. The reason for these differences across cities was that these control conditions were selected to match the other conditions (CEVMS and standard billboards) that the drivers would experience in the two respective cities. Also, the selection of DCZs was obviously constrained by what was available on the ground in these cities. The results for the off-premise advertising conditions are consistent with Lee et al., who observed that 76 percent of drivers’ time was spent looking at the road ahead in the CEVMS scenario and 75 percent in the standard billboard scenario. (9) However, it should be kept in mind 54 that drivers did gaze away from the road ahead even when no off-premise advertising was present and that the presence of clutter or salient visual stimuli did not necessarily control where drivers gazed. Do glances to CEVMS occur that would suggest a decrease in safety? In DCZs containing CEVMS, about 2.5 percent of the fixations were to CEVMS (about 2.4 percent to standard billboards). The results for fixations are similar to those reported in other field data collection efforts that included advertising signs. (12,11,9,13) Fixations greater than 2,000 ms were not observed for CEVMS or standards billboards. However, an analysis of dwell times to CEVMS showed a mean dwell time of 994 ms (maximum of 1,467 ms) for Reading and a mean of 1,039 ms (maximum of 2,270 ms) for Richmond. Statistical comparisons of average dwell times between CEVMS and standard billboards were not significant in Reading; however, in Richmond the average dwell times to CEVMS were significantly longer than to standard billboards, though below 2,000 ms. There was one dwell time greater than 2,000 ms to a CEVMS across the two cities. On the other hand, for standard billboards there were three long dwell times in Reading; there were no long dwell times to these billboards in Richmond. Review of the video data for these four long dwell times showed that the signs were not far from the forward view when participants were fixating. Therefore, the drivers still had access to information about what was in front of them through peripheral vision. As the analyses of gazes to the road ahead showed, drivers distributed their gazes away from the road ahead even when there were no off-premise billboards present. Also, drivers gazed and fixated on off-premise signs even though they were generally irrelevant to the driving task. However, the results did not provide evidence indicating that CEVMS were associated with long glances away from the road that may reflect an increase in risk. When long dwell times occurred to CEVMS or standard billboards, the road ahead was still in the driver’s field of view. Do drivers look at CEVMS more than at standard billboards? The drivers were generally more likely to gaze at CEVMS than at standard billboards. However, there was some variability between the two locations and between type of roadway (arterial or freeway). In Reading, the participants looked more often at CEVMS when on arterials, whereas they looked more often at standard billboards when on freeways. In Richmond, the drivers looked at CEVMS more than standard billboards no matter the type of road they were on, but as in Reading the preference for gazing at CEVMS was greater on arterials (68 percent on arterials and 55 percent on freeways). The slower speed on arterials and sign placement may present drivers with more opportunities to gaze at the signs. In Richmond, the results showed that drivers gazed more at CEVMS than standard billboards at night; however, for Reading no effect for time of day was found. CEVMS do have higher luminance and contrast than standard billboards at night. The results showed mean luminance of about 56 cd/m 2 in the two cities where testing was conducted. These signs would appear clearly visible but not overly bright. 55 SUMMARY The results of these studies are consistent with a wealth of research that has been conducted on vision in natural environments. (26,22,21) In the driving environment, gaze allocation is principally controlled by the requirements of the task. Consistent results were shown for the proportion of gazes to the road ahead for off-premise advertising conditions across the two cities. Average fixations were similar to CEVMS and standard billboards with no long single fixations evident for either condition. Across the two cities, four long dwell times were observed: one to a CEVMS on a freeway in the day, two to the same standard billboard on a freeway (once at night and once in the daytime), and one to a standard billboard on an arterial at night. Examination of the scene video and eye tracking data indicated that these long dwell times occurred when the billboards were close to the forward field of view where peripheral vision could still be used to gather visual information on the forward roadway. The present data suggest that the drivers in this study directed the majority of their visual attention to areas of the roadway that were relevant to the task at hand (i.e., the driving task). Furthermore, it is possible, and likely, that in the time that the drivers looked away from the forward roadway, they may have elected to glance at other objects in the surrounding environment (in the absence of billboards) that were not relevant to the driving task. When billboards were present, the drivers in this study sometimes looked at them, but not such that overall attention to the forward roadway decreased. LIMITATIONS OF THE RESEARCH In this study the participants drove a research vehicle with two experimenters on board. The participants were provided with audio turn-by-turn directions and consequently did not have a taxing navigation task to perform. The participants were instructed to drive as they normally would. However, the presence of researchers in the vehicle and the nature of the driving task do limit the degree to which one may generalize the current results to other driving situations. This is a general limitation of instrumented vehicle research. The two cities employed in the study appeared to follow common practices with respect to the content change frequency (every 8 to 10 seconds) and the brightness of the CEVMS. The current results would not generalize to situations where these guidelines are not being followed. Participant recruiting was done through libraries, community centers and at a university. This recruiting procedure resulted in a participant demographic distribution that may not be representative of the general driving population. The study employed a head-free eye tracking device to increase the realism of the driving situation (no head-mounted gear). However, the eye tracker had a sampling rate of 60 Hz, which made determining saccades problematic. The eye tracker and analyses software employed in this effort represents a significant improvement in technology over previous similar efforts in this area. The study focused on objects that were 1,000 feet or less from the drivers. This was dictated by the accuracy of the eye tracking system and the ability to resolve objects for data reduction. In addition, the geometry of the roadway precluded the consideration of objects at great distances. 56 The study was performed on actual roadways, and this limited the control of the visual scenes except via the route selection process. In an ideal case, one would have had roadways with CEVMS, standard billboards, and no off-premise advertising and in which the context surrounding digital and standard billboards did not differ. This was not the case in this study, although such an exclusive environment would be inconsistent with the experience of most drivers. This presents issues with the interpretation of the specific contributions made by billboards and the environment to the driver’s behavior. Sign content was not investigated (or controlled) in the present study, but may be an important factor to consider in future studies that investigate the distraction potential of advertising signs. Investigations about the effect of content could potentially be performed in driving simulators where this variable could be systematically controlled and manipulated. REFERENCES 1. National Highway Traffic Safety Administration. Policy Statement. [Available online at http://www.nhtsa.gov/Driving+Safety/Distracted+Driving/ci.Policy+Statement+and+Co mpiled+FAQs+on+Distracted+Driving.print.] Accessed 7/27/2012. 2. Shepherd, G. M., 2007: Guidance on Off-Premise Changeable Message Signs. http://www.fhwa.dot.gov/realestate/offprmsgsnguid.htm. 3. Scenic America. Position Paper Regarding the Propriety of Permitting Digital Billboards on Interstate and Federal-Aid Highways under the Highway Beautification Act. 2010. 4. Molino, J. A., J. Wachtel, J. E. Farbry, M. B. Hermosillo, and T. M. Granda. . The Effects of Commercial Electronic Variable Message Signs (Cevms) on Driver Attention and Distraction: An Update., FHWA-HRT-09-018. Federal Highway Administration, 2009. 5. Farbry, J., Wochinger, K., Shafer, T., Owens, N, & Nedzesky, A. Research Review of Potential Safety Effects of Electronic Billboards on Driver Attention and Distraction. Federal Highway Administration. Washington, DC, 2001. 6. Tantala, M. W., and A. M. Tantala. A Study of the Relationship between Digital Billboards and Traffic Safety in Henrico County and Richmond, Virginia. The Foundation for Outdoor Advertising Research and Education (FOARE), 2010. 7. Tantala, M. W., and A. M. Tantala. An Examination of the Relationship between Digital Billboards and Traffic Safety in Reading, Pennsylvania Using Empirical Bayes Analyses. Moving Toward Zero 2100. ITE Technical Conference and Exhibit, Buena Vista, FL, Institute of Transportation Engineers, 2011. 8. Elvik, R. The Predictive Validity of Empirical Bayes Estimates of Road Safety. Accident Analysis & Prevention, 40, 2008, 1964-1969. 9. Lee, S. E., McElheny, M.J., & Gibbons, R. . Driving Performance and Digital Billboards. Report prepared for Foundation for Outdoor Advertising Research and Education. Virginia Tech Transportation Institute., 2007. 10. Society of Automotive Engineers. Definitions and Experimental Measures Related to the Specification of Driver Visual Behavior Using Video Based Techniques. 2000. 11. Beijer, D., A. Smiley, and M. Eizenman. Observed Driver Glance Behavior at Roadside Advertising Signs. Transportation Research Record: Journal of the Transportation Research Board,, No. 1899, 2004, 96-103. 12. Smiley, A., T. Smahel, and M. Eizenman. Impact of Video Advertising on Driver Fixation Patters. Transportation Research Record: Journal of the Transportation Research Board,, No. 1899, 2004, 76-83. 13. Kettwich, C., K. Klinger, and U. Lemmer. Do Advertisements at the Roadside Distract the Driver? Optical Sensors 2008, San Diego, CA, SPIE, 2008. 14. Klauer, S. G., Dingus, T. A., Neale, V. L., Sudweeks, J.D., & Ramsey, D.J. The Impact of Driver Inattention on near-Crash/Crash Risk: An Analysis Using the 100-Car Naturalistic Driving Study Data, DOT HS 810 594. National Highway Traffic Safety Administration, 2006. 15. Chattington, M., N. Reed, D. Basacik, A. Flint, and A. Parkes. Investigating Driver Distraction: The Effects of Video and Static Advertising, PPR409. Transport Research Laboratory, 2009. 58 16. Kettwich, C., K. Klinger, and U. Lemmer, 2008: Do Advertisements at the Roadside Distract the Driver? Optical Sensors 2008, F. Berghmans, A. G. Mignani, A. Cutolo, P. P. Moyrueis, and T. P. Pearsall, Eds., SPIE. 17. Cole, B. L., and P. K. Hughes. A Field Trial of Attention and Search Conspicuity. Human Factors, 26, 1984, 299-313. 18. Ruz, M., and J. Lupiáñez. A Review of Attentional Capture: On Its Automaticity and Sensitivity to Endogenous Control. Psicológica, 23, 2002, 283-309. 19. Wachtel, J. Safety Impacts of the Emerging Digital Display Technology for Outdoor Advertising Signs. The Veridian Group, Inc, 2009. 20. Theeuwes, J., and R. Burger. Attentional Control During Visual Search: The Effect of Irrelevant Singletons. Journal of Experimental Psychology: Human Perception and Performance, 24, 1998, 1342-1353. 21. Tatler, B. W., M. M. Hayhoe, M. F. Land, and D. H. Ballard. Eye Guidance in Natural Vision: Reinterpreting Salience. Journal of Vision, 11, 2011, 1-23. 22. Land, M. F. Vision, Eye Movements, and Natural Behavior. Visual Neuroscience, 26, 2009, 51-62. 23. Eckstein, M. P. Visual Search: A Retrospective. Journal of Vision, 11, 2011, 1-36. 24. Henderson, J., G. Malcolm, and C. Schandl. Searching in the Dark: Cognitive Relevance Drives Attention in Real-World Scenes. Psychonomic Bulletin & Review, 16, 2009, 850- 856. 25. Henderson, J. M., J. R. Brockmole, M. S. Castelhano, and M. Mack, 2007: Visual Saliency Does Not Account for Eye Movements During Visual Search in Real-World Scenes. Eye Movements: A Window on Mind and Brain, R. P. G. v. Gompel, M. H. Fischer, W. S. Murray, and R. L. Hill, Eds., Elsevier, 537-562. 26. Land, M. F. Eye Movements and the Control of Actions in Everyday Life. Progress in Retinal and Eye Research, 25, 2006, 296-324. 27. Hayhoe, M., and D. Ballard. Eye Movements in Natural Behavior. Trends in Cognitive Sciences, 9, 2005, 188-194. 28. Jovancevic-Misic, J., and M. Hayhoe. Adaptive Gaze Control in Natural Environments. The Journal of Neuroscience, 29, 2009, 6234-6238. 29. Shinoda, H., M. M. Hayhoe, and A. Shrivastava. What Controls Attention in Natural Environments? Vision Research, 41, 2001, 3535-3545. 30. SmartEye. Smarteye. [Available online at http://www.smarteye.se/.] Accessed June 22, 2012. 31. Whittle, P., Ed., 1994: The Psychophysics of Contrast Brightness. Lawrence Erlbaum Associates. 32. Regan, M. A., K. L. Young, J. D. Lee, and C. P. Gordon, 2009: Sources of Driver Distraction. Driver Distraction: Theory, Effects, and Mitigation., M. A. Regan, J. D. Lee, and K. L. Young, Eds., CRC Press, Taylor & Francis Group. 33. Horberry, T., & Edquist, J., 2009: Distractions Outside the Vehicle. Driver Distraction: Theory, Effects, and Mitigation., M. A. Regan, Lee, J.D., & Young, K.L., Ed., CRC Press, Taylor & Francis Group. 34. Rosenholtz, R., Y. Li, and L. Nakano. Measuring Visual Clutter. J Vis, 7, 2007, 17 11-22. 35. Bravo, M. J., and H. Farid. A Scale Invariant Measure of Clutter. Journal of Vision, 8, 2008, 1-9. 59 36. EyesDx. Multiple-Analysis of Psychophysical and Performance Signals (Mapps) [Available online at http://www.eyesdx.com/.] Accessed June 22, 2012. 37. Recarte, M. A., and L. M. Nunes. Effects of Verbal and Spatial-Imagery Tasks on Eye Fixations While Driving. Journal of Experimental Psychology: Applied, 6, 2000, 31-43. 38. Manor, B. R., and E. Gordon. Defining the Temporal Threshold for Ocular Fixation in Free-Viewing Visuocognitive Tasks. Journal of Neuroscience Methods, 128, 2003, 85- 93. 39. Ahlstrom, C., K. Kircher, and A. Kircher. Considerations When Calculating Percent Road Centre from Eye Movement Data in Driver Distraction Monitoring. Proceedings of the Fifth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, 2009, 132-139. 40. Agresti, A., 2002: Analyzing Repeated Categorical Response Data. Categorical Data Analysis, 2nd Edition, D. J. Balding, Ed., John Wiley & Sons, Inc. 41. Stokes, M. E., C. S. Davis, and G. G. Koch. Categorical Data Analysis Using the Sas System (2nd Ed.). SAS Institute, Inc., Cary, NC, 2000. 42. Molenbergs, G., and G. Verbeke. Likelihood Ratio, Score, and Wald Tests in a Constrained Parameter Space. The American Statistican, 61, 2007, 22-27. 43. ISO, 2002: Road Vehicles — Measurement of Driver Visual Behaviour with Respect to Transport Information and Control Systems —Part 1: Definitions and Parameters. ISO. 44. ISO, 2001: Road Vehicles — Measurement of Driver Visual Behaviour with Respect to Transport Information and Control Systems — Part 2: Equipment and Procedures. ISO. 45. Highway Capacity Manual. Transportation Research Board, Washington, DC, 2000. 46. Itti, L., and C. Koch. A Saliency-Based Search Mechanism for Overt and Covert Shifts of Visual Attention. Vision Research, 40, 2000, 1489-1506. 47. Walther, D., and C. Koch. Modeling Attention to Salient Proto-Objects. Neural Networks, 19, 2006, 1395-1407. 48. Itti, L., C. Koch, and E. Niebur. A Model of Saliency-Based Visual Attention for Rapid Scene Analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 20, 1998, 1254-1259. 49. Walther, D. B. Saliency Toolbox. [Available online at http://www.saliencytoolbox.net/index.html.] Accessed 6/27/2012. 50. Land, M. F. Predictable Eye-Head Coordination During Driving. Nature, 359, 1992, 318- 320. 51. Balas, B., L. Nakano, and R. Rosenholtz. A Summary-Statistic Representation in Peripheral Vision Explains Visual Crowding. Journal of Vision, 9, 2009, 1-18. 52. Levi, D. M. Crowding--an Essential Bottleneck for Object Recognition: A Mini-Review. Vision Research, 48, 2008, 635-654. 53. Horrey, W. J., and C. D. Wickens. In-Vehicle Glance Duration: Distributions,Tails, and Model of Crash Risk. Transportation Research Record, 2018, 2007, 22-28. 54. Wierwille, W. W., 1993: Visual and Manual Demands of in-Car Controls and Displays. Automotive Ergonomics, B. Peacock, and W. Karwowsk, Eds., Taylor and Francis, 299- 320. 55. Strasburger, H., I. Rentschler, and M. Jüttner. Peripheral Vision and Pattern Recognition: A Review. Journal of Vision, 11, 2011, 1-82. 60 56. Reimer, B. Impact of Cognitive Task Complexity on Drivers’ Visual Tunneling. Transportation Research Record: Journal of the Transportation Research Board, No. 2138, 2009, 13–19. 57. Rayner, K., A. W. Inhoff, R. E. Morrison, M. L. Slowiaczek, and J. H. Bertera. Masking of Foveal and Parafoveal Vision During Eye Fixations in Reading. Journal of Experimental Psychology: Human Perception and Performance, 7, 1981, 167-179. 58. Larson, A. M., and L. C. Loschky. The Contributions of Central Versus Peripheral Vision to Scene Gist Recognition. Journal of Vision, 9, 2009, 1-16. Digital Billboards: Agents for Safety Gino Sesto Founder, DASH TWO The nation’s highway safety agency recently featured a digital billboard on the cover of its newsletter, heralding winners of its contest to design safe-driving messages on billboards (Project Yellow Light). ATTACHMENT 3 Yes, digital billboards have become a communications platform for the sake of safety (safe driving and overall safety of communities). Accepted, regulated, and safe, digital billboards have been deployed for nearly two decades. We thought it would be instructive — and historically interesting — to chart the history of digital billboards and traffic safety. Industry as Research Pioneer In 2007, Virginia Tech published an industry-sponsored study based on driver behavior that said digital billboards were safety neutral. The industry went further, tasking outside experts to analyze accident records in five different markets. Reams of data showed that digital billboards did not have a statistical relationship with accidents. Healthy skepticism greets most industry-sponsored research. The Federal Highway Administration (FHWA) launched its own study, enlisting the respected global engineering firm SAIC (Science Applications International Corporation) to help analyze drivers’ eye glances. Using the latest equipment to monitor eye movements, the federal study (released in late 2013) concluded that glances in the direction of digital billboards were well under the distraction threshold set earlier via comprehensive federal research. What Do the Feds Say about Safety? A citizen from Georgia asked federal authorities about digital billboards and safety. Here’s the response she received: From: Lori Millen <Lori.Millen@dot.gov> Date: Wednesday, November 23, 2016 at 2:41 PM To: “info@stopjcbillboards.org” <info@stopjcbillboards.org> Subject: Johns Creek Billboards Dear Ms. Murphy: Thank you for sharing your concerns about the Stop the Texts. Stop the Wrecks. digital billboards in Johns Creek, GA. Your emails to TSM, Susan McMeen, and NHTSA’s Region 4 office were forwarded to me for a response. Please be advised that the US Department of Transportation’s Federal Highway Administration (FHWA) contracted the Science Applications International Corporation (SAIC) to study the effects of digital billboards on driver attention and distraction in 2007. This study, which was based on how long drivers took their eyes off the road when in the presence of digital billboards, was conducted to determine if digital billboards posed an unsafe driver distraction. The study was completed in early 2010, and a draft report was subjected to peer review in 2012. On December 30, 2013, FHWA released its final report that included the following findings:  The presence of digital billboards does not appear to be related to a decrease in looking toward the road ahead, which is consistent with earlier industry-sponsored field research studies done by the Virginia Technical Transportation Institute (VTTI).  The longest fixation to a digital billboard was 1.34 seconds, and to a standard billboard, it was 1.28 seconds, both of which are well below the accepted standard. The results of this study are consistent with a wealth of research that has been conducted on vision in natural environments. In the driving environment, gaze allocation is principally controlled by the requirements of the task. The present data suggest that the drivers in this study directed the majority of their visual attention to areas of the roadway that were relevant to the task at hand (i.e., the driving task). Furthermore, it is possible, and likely, that in the time that the drivers looked away from the forward roadway, they may have elected to glance at other objects in the surrounding environment (in the absence of billboards) that were not relevant to the driving task. When billboards were present, the drivers in this study sometimes looked at them, but not such that overall attention to the forward roadway decreased. FHWA has authority over issues relating to outdoor signage. If you have any additional questions or concerns, I would encourage you to contact Melissa Corder at melissa.corder@dot.gov. She can provide you with more detailed information about the FHWA studies. I hope this information has been helpful to you, and thank you for your interest in highway safety. Sincerely, Lori Gabrielle Millen, Marketing Specialist U.S. Department of Transportation/NHTSA 1200 New Jersey Avenue, S.E. W52-234 Washington, D.C. 20590 Government Relies on Digital Billboards for Safety In April, the FBI gave a director’s award to Clear Channel-Las Vegas for helping law enforcement after the mass shooting. For more than a decade, the FBI and other law enforcement have used digital billboards to empower the public on behalf of safety. In 2010, pro-Transportation Secretary Ray LaHood launched high-visibility enforcement projects in two test markets to reduce distracted driving (Hartford and Syracuse). After four waves of intense enforcement and public education, including public service messages on digital billboards in Hartford, results were significant. In Hartford, drivers using hand-held cell phones while driving dropped 57 percent, and texting while driving declined 72 percent, according to NHTSA’s report in 2017. Last month, the City of Albuquerque (NM) enacted a program to display real-time emergency alerts on 44 digital billboards across the region. Similar partnerships have been enacted by other cities and several states. The government’s top emergency manager described digital billboards like this: “Use of digital signage along highways is part of the layering and unity of messaging to reach affected communities, and supplements radio and mobile alerts,” said William B. “Brock” Long, administrator of the Federal Emergency Management Agency (FEMA). Those who operate digital billboards are proud of their safety record, and their ability to advance public safety. ATTACHMENT 4 Environmental Services 222 Laporte Avenue Fort Collins, CO 80521 970.221-6600 fcgov.com MEMORANDUM NATURAL RESOURCES ADVISORY BOARD DATE: October 26, 2018 TO: Mayor and City Council Members FROM: Natural Resources Advisory Board (NRAB) SUBJECT: Digital Signs Code Change On September 19, 2018 the Natural Resources Advisory Board (hereafter NRAB) voted in support of the proposed changes to city code relating to the installation of digital signs. These changes are intended to encourage the reduction of visual clutter by allowing up to 5 digital signs within city limits providing that either 8 non-digital (static) signs or a total of 2200 square feet of static signs (whichever is greater) is removed for each digital sign installation. The NRAB supports the reduction of billboard advertising through this replacement as we think this will improve the visual characteristic of our city. We do have some concerns that we feel are relevant to this change. The most important concern is the impact of light from the digital billboards in regards to Fort Collins’ Dark Sky goals. As static signs are being considered for replacement those with existing illumination should be of the highest priority for removal. This will ensure that there is less impact to night lighting. Additionally, if there were means of reducing light spillage from these new digital signs such as baffles to direct the light downwards that would be preferable. A second concern is placement of new digital signs in regards to natural areas and parks. The language in the proposed code change reads as follows: “Not located within 500 ft. from designated historic districts or landmarks, natural areas or parks, or property that is used or zoned for single-family, duplex, or single family attached residential uses.“ We would like to ensure users of natural areas and parks as well as the wildlife inhabiting these areas are impacted as little as possible by these digital signs. We would like to see language within the code that makes it illegal to have signs face into any natural area. The detrimental effect of light pollution has been well documented for humans, plants and animals12 . Users of natural areas visit those areas to distance themselves from modern intrusions and we would like that experience to be without the glare and distraction from such signs. Finally, we would discourage the placement of digital signs in our gateway areas (Harmony Rd., Prospect Rd., and Mulberry Rd.) as we feel this would diminish the tone and feeling these entrances into our community have on visitors and residents alike. Respectfully Submitted, Luke Caldwell Vice-Chair, Natural Resources Advisory Board 1 Chepesiuk, R. Missing the dark: health effects of light pollution. 2009. Environmental Health Perspectives. 2 Longcore, T. & Rich, C. Ecological light pollution.2004. Frontiers in Ecology and the Environment. ATTACHMENT 5 Missing the Dark Health Effects of Light Pollution Environews | Focus A 20 VOLUME 117 | NUMBER 1 | January 2009 t Environmental Health Perspectives ATTACHMENT 6 Focus | Missing the Dark Mark A. Johnson/Alamy Aerial view of Los Angeles, California In 1879, Thomas Edison’s incandescent light bulbs first illuminated a New York street, and the modern era of electric lighting began. Since then, the world has become awash in electric light. Powerful lamps light up streets, yards, parking lots, and bill- boards. Sports facilities blaze with light that is visible for tens of miles. Business and office building windows glow throughout the night. According to the Tucson, Arizona–based International Dark-Sky Association (IDA), the sky glow of Los Angeles is visible from an airplane 200 miles away. In most of the world’s large urban centers, stargazing is something that happens at a planetarium. Indeed, when a 1994 earthquake knocked out the power in Los Angeles, many anxious residents called local emergency centers to report seeing a strange “giant, silvery cloud” in the dark sky. What they were really seeing—for the first time—was the Milky Way, long obliterated by the urban sky glow. None of this is to say that electric lights are inher- ently bad. Artificial light has benefited society by, for instance, extending the length of the productive day, offering more time not just for working but also for rec- reational activities that require light. But when artificial outdoor lighting becomes inefficient, annoying, and unnecessary, it is known as light pollution. Many envi- ronmentalists, naturalists, and medical researchers con- sider light pollution to be one of the fastest growing and most pervasive forms of environmental pollution. And a growing body of scientific research suggests that light Focus | Missing the Dark A 22 VOLUME 117 | NUMBER 1 | January 2009 t Environmental Health Perspectives T odd Carlson pollution can have lasting adverse effects on both human and wildlife health. When does nuisance light become a health hazard? Richard Stevens, a professor and cancer epidemiologist at the University of Connecticut Health Center in Farm ington, Connecticut, says light photons must hit the retina for biologic effects to occur. “However, in an environment where there is much artifi- cial light at night—such as Manhattan or Las Vegas—there is much more opportunity for exposure of the retina to photons that might disrupt circadian rhythm,” he says. “So I think it is not only ‘night owls’ who get those photons. Almost all of us awaken during the night for periods of time, and unless we have blackout shades there is some electric lighting coming in our windows. It is not clear how much is too much; that is an important part of the research now.” According to “The First World Atlas of the Artificial Night Sky Brightness,” a report on global light pollution published in volume 328, issue 3 (2001) of the Monthly Notices of the Royal Astronomical Society, two- thirds of the U.S. population and more than one-half of the European population have already lost the ability to see the Milky Way with the naked eye. Moreover, 63% of the world population and 99% of the popula- tion of the European Union and the United States (excluding Alaska and Hawaii) live in areas where the night sky is brighter than the threshold for light-polluted status set by the International Astronomical Union—that is, the artificial sky brightness is greater than 10% of the natural sky brightness above 45° of elevation. Light pollution comes in many forms, including sky glow, light trespass, glare, and over illumination. Sky glow is the bright halo that appears over urban areas at night, a product of light being scattered by water droplets or particles in the air. Light tres- pass occurs when unwanted artificial light from, for instance, a floodlight or streetlight spills onto an adjacent property, lighting an area that would otherwise be dark. Glare is created by light that shines horizontally. Overillumination refers to the use of artificial light well beyond what is required for a spe- cific activity, such as keeping the lights on all night in an empty office building. Distracted by the Light The ecologic effects of artificial light have been well documented. Light pollution has Focus | Missing the Dark Environmental Health Perspectives t VOLUME 117 | NUMBER 1 | January 2009 A 23 F igure 1: U.S. National Park Service, Matthew Ray/EHP; figures 2–4: International Dark-Sky Association According to the National Park Service, 50% of the light from a typical unshielded light fixture is wasted, shining upward where it is not needed (figure 1). About 40% of the light shines downward to illu- minate the intended target. Light emitted horizontally tends to create glare. Globe lights typically distribute light poorly and contribute to glare (figure 2). Flood- lights can fill a space with light, but they may be too bright for their intended task, and much of the light is wasted (figure 3). Good lighting is shielded in a manner that directs all the light where it is needed and wanted. The International Dark-Sky Association (IDA) recommends that all lighting be installed such that no light is emitted above a horizontal plane running through the lowest part of the fixture (figure 4). IDA further recommends the use of low- pressure sodium (LPS) lights wherever pos- sible. LPS lights are the most energy-effi- cient lights currently available. They emit a yellow light at the wavelength where the human eye is most sensitive, but the monochromatic light makes it difficult to distinguish the colors of objects below. For outdoor lighting where color percep- tion is important (to enhance security, for instance), IDA recommends high-pressure sodium lights. How Outdoor Lighting Translates into Light Pollution 50% Wasted Light Productive 40% Light 10% Glare 1 2 3 4 Focus | Missing the Dark A 24 VOLUME 117 | NUMBER 1 | January 2009 t Environmental Health Perspectives L ynda Richardson/Corbis works to safeguard migratory birds in the urban environment. “It is a serious situa- tion because many species that collide fre- quently are known to be in long-term decline and some are already designated officially as threatened.” Each year in New York City alone, about 10,000 migratory birds are injured or killed crashing into skyscrapers and high-rise build- ings, says Glenn Phillips, executive director of the New York City Audubon Society. The estimates as to the number of birds dying from collisions across North America annu- ally range from 98 million to close to a billion. The U.S. Fish and Wildlife Service estimates 5–50 million birds die each year from collisions with communication towers. Turtles and birds are not the only wildlife affected by artificial nighttime lighting. Frogs have been found to inhibit their mating calls when they are exposed to excessive light at night, reducing their reproductive capacity. The feeding behavior of bats also is altered by artificial light. Researchers have blamed light pollution for declines in populations of North American moths, according to Ecologi- cal Consequences of Artificial Night Lighting. Almost all small rodents and carnivores, 80% of marsupials, and 20% of primates are noc- turnal. “We are just now understanding the nocturnality of many creatures,” says Chad Moore, Night Sky Program manager with the National Park Service. “Not protecting the night will destroy the habitat of many animals.” Resetting the Circadian Clock The health effects of light pollution have not been as well defined for humans as for wildlife, although a compelling amount of epidemiologic evi- dence points to a consistent association between exposure to indoor artificial nighttime light and health problems such as breast cancer, says George Brainard, a professor of neurology at Jefferson Medical College, Thomas Jef- Focus | Missing the Dark Environmental Health Perspectives t VOLUME 117 | NUMBER 1 | January 2009 A 25 Beyond Sleep Disorders Alteration of the circadian clock can branch into other effects besides sleep disorders. A team of Vanderbilt University research- ers considered the possibility that constant artificial light exposure in neo natal inten- sive care units could impair the developing circadian rhythm of premature babies. In a study published in the August 2006 issue of Pediatric Research, they exposed new- born mice (comparable in development to 13-week-old human fetuses) to constant artificial light for several weeks. The exposed mice were were unable to maintain a coher- ent circadian cycle at age 3 weeks (compa- rable to a full-term human neonate). Mice exposed for an additional 4 weeks were unable to establish a regular activity cycle. The researchers concluded that excessive artificial light exposure early in life might contribute to an increased risk of depression and other mood disorders in humans. Lead researcher Douglas McMahon notes, “All this is speculative at this time, but certainly the data would indicate that human infants benefit from the synchronizing effect of a normal light/dark cycle.” Increase in Artiﬁcial Night Sky Brightness in North America Late 1950s 1997 2025 Mid 1970s Artiﬁcial night sky brightness at zenith, at sea level, for a standard clean atmosphere as a fraction of the average natural night sky brightness. These maps are based on upward light measured by the Defense Meteorological Satellite Program after accounting for propagation and scattering of that light in the atmosphere. The 2025 map assumes a constant population growth rate of 6% per year. Source: http://www.lightpollution.it/ © 2001 P. Cinzano, F. Falchi, C.D. Elvidge <11% above the natural brightness level 11–33% above the natural brightness level 34–99% above the natural brightness level 100% above the natural brightness level 3–9 times the natural brightness level (the Milky Way is no longer visible) 9–27 times the natural brightness level (fewer than 100 stars are visible) 27–81 times the natural brightness level (the North Star is no longer visible) 81–243 times the natural brightness level (the Big Dipper is no longer visible) Focus | Missing the Dark A 26 VOLUME 117 | NUMBER 1 | January 2009 t Environmental Health Perspectives A kira Suemori/AP Photo Since 1995, studies in such journals as Epidemi- ology, Cancer Causes and Control, the Journal of the National Cancer Institute, and Aviation Space Environ- mental Medicine, among oth- ers, have examined female employees working a rotat- ing night shift and found that an elevated breast can- cer risk is associated with occupational exposure to artificial light at night. Mari- ana Figueiro, program direc- tor at the Lighting Research Center of Rensselaer Poly- technic Institute in Troy, New York, notes that per- manent shift workers may be less likely to be disrupted by night work because their circadian rhythm can read- just to the night work as long as light/dark patterns are controlled. In a study published in the 17 October 2001 Jour- nal of the National Cancer Institute, Harvard Univer- sity epidemiologist Eva S. Schernhammer and col- leagues from Brigham and Women’s Hospital in Boston used data from the 1988 Nurses’ Health Study (NHS), which surveyed 121,701 registered female nurses on a range of health issues. Schernhammer and her colleagues found an association between breast cancer and shift work that was restricted to women who had worked 30 or more years on rotating night shifts (0.5% of the study population). In another study of the NHS cohort, Schernhammer and colleagues also found elevated breast cancer risk associated with rotating night shift work. Discussing this finding in the January 2006 issue of Epide- miology, they wrote that shift work was asso- ciated with only a modest increased breast cancer risk among the women studied. The researchers further wrote, however, that their study’s findings “in combination with the results of earlier work, reduce the likelihood that this association is due solely to chance.” Schernhammer and her colleagues have Focus | Missing the Dark Environmental Health Perspectives t VOLUME 117 | NUMBER 1 | January 2009 A 27 “we need to understand what’s going on as soon as possible.” Linking Light Pollution to Human Health The evidence that indoor artificial light at night influences human health is fairly strong, but how does this relate to light pollution? The work in this area has just begun, but two studies in Israel have yielded some intriguing findings. Stevens was part of a study team that used satellite photos to gauge the level of nighttime artificial light in 147 communities in Israel, then overlaid the photos with a map detailing the distribution of breast cancer cases. The results showed a statistically significant cor- relation between outdoor artificial light at night and breast cancer, even when control- ling for population density, affluence, and air pollution. Women living in neighbor- hoods where it was bright enough to read a book outside at midnight had a 73% higher risk of developing breast cancer than those residing in areas with the least outdoor arti- ficial lighting. However, lung cancer risk was not affected. The findings appeared in the January 2008 issue of Chronobiology International. “It may turn out that artificial light expo- sure at night increases risk, but not entirely by the melatonin mechanism, so we need to do more studies of ‘clock’ genes—nine have so far been identified—and light exposure in rodent models and humans,” Stevens says. Clock genes carry the genetic instructions to produce protein products that control circa- dian rhythm. Research needs to be done not just on the light pollution–cancer connection but also on several other diseases that may be influenced by light and dark. Travis Longcore, co-editor of Ecological Consequences of Artificial Night Lighting and a research associate professor at the University of Southern California Center for Sustain- able Cities, suggests two ways outdoor light pollution may contribute to artificial light– associated health effects in humans. “From a human health perspective, it seems that we are concerned with whatever increases artifi- cial light exposure indoors at night,” he says. “The effect of outdoor lighting on indoor exposure could be either direct or indirect. In the direct impact scenario, the artificial light from outside reaches people inside at night at levels that affect production of hormones. In an indirect impact it would disturb people inside, who then turn on lights and expose themselves to more light.” As diurnal creatures, humans have long sought methods to illuminate the night. In pre-industrial times, artificial light was generated by burning various materials, including wood, oil, and even dried fish. While these methods of lighting certainly influenced animal behavior and ecology locally, such effects were limited. The relatively recent invention and rapid prolif- eration of electric lights, however, have transformed the nighttime environment over substantial portions of the Earth’s surface. Ecologists have not entirely ignored the potential dis- ruption of ecological systems by artificial night lighting. Several authors have written reviews of the potential effects on ecosystems or taxonomic groups, published in the “gray” literature (Health Council of the Netherlands 2000; Hill 1990), conference proceedings (Outen 2002; Schmiedel 2001), and journal articles (Frank 1988; Verheijen 1985; Salmon 2003). This review attempts to integrate the literature on the topic, and draws on a con- ference organized by the authors in 2002 titled Ecological Consequences of Artificial Night Lighting. We identify the roles that artificial night lighting plays in changing eco- logical interactions across taxa, as opposed to reviewing these effects by taxonomic group. We first discuss the scale and extent of ecological light pollution and its relation- ship to astronomical light pollution, as well as the mea- surement of light for ecological research. We then address the recorded and potential influences of artificial night lighting within the nested hierarchy of behavioral and population ecology, community ecology, and ecosystem ecology. While this hierarchy is somewhat artificial and certainly mutable, it illustrates the breadth of potential consequences of ecological light pollution. The important effects of light on the physiology of organisms (see Health Council of the Netherlands 2000) are not discussed here. Astronomical and ecological light pollution: scale and extent The term “light pollution” has been in use for a number of years, but in most circumstances refers to the degrada- tion of human views of the night sky. We want to clarify that this is “astronomical light pollution”, where stars and other celestial bodies are washed out by light that is either directed or reflected upward. This is a broad-scale phenomenon, with hundreds of thousands of light sources cumulatively contributing to increased nighttime illumi- nation of the sky; the light reflected back from the sky is called “sky glow” (Figure 1). We describe artificial light that alters the natural patterns of light and dark in ecosys- tems as “ecological light pollution”. Verheijen (1985) proposed the term “photopollution” to mean “artificial light having adverse effects on wildlife”. Because pho- topollution literally means “light pollution” and because light pollution is so widely understood today to describe the degradation of the view of the night sky and the human experience of the night, we believe that a more descriptive term is now necessary. Ecological light pollu- tion includes direct glare, chronically increased illumina- 191 © The Ecological Society of America www.frontiersinecology.org REVIEWS REVIEWS REVIEWS Ecological light pollution T Longcore and C Rich tion, and temporary, unexpected fluctuations in light- ing. Sources of ecological light pollution include sky glow, lighted buildings and towers, streetlights, fishing boats, security lights, lights on vehicles, flares on off- shore oil platforms, and even lights on undersea research vessels, all of which can disrupt ecosystems to varying degrees. The phenomenon therefore involves potential effects across a range of spatial and temporal scales. The extent of ecological light pollution is global (Elvidge et al. 1997; Figure 2). The first atlas of artificial night sky brightness illustrates that astronomical light pollution extends to every inhabited continent (Cinzano et al. 2001). Cinzano et al. (2001) calculate that only 40% of Americans live where it becomes sufficiently dark at night for the human eye to make a complete transition from cone to rod vision and that 18.7% of the terrestrial surface of the Earth is exposed to night sky brightness that is polluted by astronomical standards. Ecosystems may be affected by these levels of illumina- tion and lights that do not contribute to sky glow may still have ecological consequences, ensuring that ecolog- ical light pollution afflicts an even greater proportion of the Earth. Lighted fishing fleets, offshore oil platforms, and cruise ships bring the disruption of artificial night lighting to the world’s oceans. The tropics may be especially sensitive to alterations in natural diel (ie over a 24-hour period) patterns of light and dark because of the year-round constancy of daily cycles (Gliwicz 1999). A shortened or brighter night is more likely to affect tropical species adapted to diel pat- terns with minimal seasonal variation than extratropical species adapted to substantial seasonal variation. Of course, temperate and polar zone species active only dur- ing a portion of the year would be excluded from this gen- eralization. Species in temperate zones will also be susceptible to disruptions if they depend on seasonal day length cues to trigger critical behaviors. Measurements and units Measurement of ecological light pollution often involves determination of illumination at a given place. Illumination is the amount of light incident per unit area – not the only measurement relevant to ecological light pol- lution, but the most common. Light varies in intensity (the number of photons per unit area) and spectral content (expressed by wavelength). Ideally, ecologists should mea- sure illumination in photons per square meter per second with associated measurements of the wavelengths of light present. More often, illumination is measured in lux (or footcan- dles, the non-SI unit), which expresses the brightness of light as perceived by the human eye. The lux measurement places more emphasis on wavelengths of light that the human eye detects best and less on those that humans perceive poorly. Because other organisms perceive light differently – including wave- T Longcore and C Rich Ecological light pollution Behavioral and population ecology Ecological light pollution has demonstrable effects on the behavioral and population ecology of organisms in natural settings. As a whole, these effects derive from changes in ori- entation, disorientation, or misorientation, and attraction or repulsion from the altered light environment, which in turn may affect foraging, reproduction, migration, and communi- cation. Orientation/disorientation and attraction/repulsion Orientation and disorientation are responses to ambient illumination (ie the amount of light incident on objects in an environment). In contrast, attraction and repulsion occur in response to the light sources themselves and are therefore responses to luminance or the brightness of the source of light (Health Council of the Netherlands 2000). Increased illumination may extend diurnal or crepuscular behaviors into the nighttime environment by improving an animal’s ability to orient itself. Many usually diurnal birds (Hill 1990) and reptiles (Schwartz and Henderson 1991), for example, forage under artificial lights. This has been termed the “night light niche” for reptiles and seems benefi- cial for those species that can exploit it, but not for their prey (Schwartz and Henderson 1991). In addition to foraging, orientation under artificial illumi- nation may induce other behaviors, such as territorial singing in birds (Bergen and Abs 1997). For the northern mockingbird (Mimus polyglottos), males sing at night before mating, but once mated only sing at night in artificially lighted areas (Derrickson 1988) or during the full moon. The effect of these light-induced behaviors on fitness is unknown. Constant artificial night lighting may also disorient organisms accustomed to navigating in a dark environment. The best-known example of this is the disorientation of hatchling sea turtles emerging from nests on sandy beaches. Under normal circumstances, hatchlings move away from low, dark silhouettes (historically, those of dune vegeta- tion), allowing them to crawl quickly to the ocean. With beachfront lighting, the silhouettes that would have cued movement are no longer perceived, resulting in disorienta- tion (Salmon et al. 1995). Lighting also affects the egg-lay- ing behavior of female sea turtles. (For reviews of effects on sea turtles, see Salmon 2003 and Witherington 1997). Changes in light level may disrupt orientation in noctur- nal animals. The range of anatomical adaptations to allow night vision is broad (Park 1940), and rapid increases in light can blind animals. For frogs, a quick increase in illumi- nation causes a reduction in visual capability from which the recovery time may be minutes to hours (Buchanan 1993). After becoming adjusted to a light, frogs may be attracted to it as well (Jaeger and Hailman 1973; Figure 3). Birds can be disoriented and entrapped by lights at night (Ogden 1996). Once a bird is within a lighted zone at night, it may become “trapped” and will not leave the lighted area. Large numbers of nocturnally migrating birds are therefore affected when meteorological conditions bring them close to lights, for instance, during inclement weather or late at night when they tend to fly lower. 193 Ecological light pollution T Longcore and C Rich Within the sphere of lights, birds may collide with each other or a structure, become exhausted, or be taken by predators. Birds that are waylaid by buildings in urban areas at night often die in collisions with windows as they try to escape during the day. Artificial lighting has attracted birds to smokestacks, lighthouses (Squires and Hanson 1918), broadcast towers (Ogden 1996), boats (Dick and Donaldson 1978), greenhouses, oil platforms (Wiese et al. 2001), and other structures at night, resulting in direct mortality, and thus inter- fering with migration routes. Many groups of insects, of which moths are one well-known example (Frank 1988), are attracted to lights. Other taxa showing the same attraction include lacewings, beetles, bugs, caddisflies, crane flies, midges, hoverflies, wasps, and bush crickets (Eisenbeis and Hassel 2000; Kolligs 2000; Figure 4). Attraction depends on the spec- trum of light – insect collectors use ultraviolet light because of its attractive qualities – and the char- acteristics of other lights in the vicinity. Nonflying arthropods vary in their reaction to lights. Some nocturnal spiders are negatively phototactic (ie repelled by light), whereas others will exploit light if avail- able (Nakamura and Yamashita 1997). Some insects are always positively phototactic as an adaptive behavior and others always photonegative (Summers 1997). In arthro- pods, these responses may also be influenced by the frequent correlations between light, humidity, and temperature. Natural resource managers can exploit the responses of animals to lights. Lights are sometimes used to attract fish to ladders, allowing them to bypass dams and power plants (Haymes et al. 1984). Similarly, lights can attract larval fish to coral reefs (Munday et al. 1998). In the terrestrial realm, dispersing mountain lions avoid lighted areas to such a degree that Beier (1995) suggests installing lights to deter them from entering habitats dead-ending in areas where humans live. Reproduction Reproductive behaviors may be altered by artificial night lighting. Female Physalaemus pustulosus frogs, for exam- ple, are less selective about mate choice when light levels are increased, presumably preferring to mate quickly and avoid the increased predation risk of mating activity (Rand et al. 1997). Night lighting may also inhibit amphibian movement to and from breeding areas by stim- ulating phototactic behavior. Bryant Buchanan (pers comm) reports that frogs in an experimental enclosure stopped mating activity during night football games, when lights from a nearby stadium increased sky glow. Mating choruses resumed only when the enclosure was covered to shield the frogs from the light. T Longcore and C Rich Ecological light pollution (2000) investigated the effects of roadway lighting on black-tailed godwits (Limosa l. limosa) in wet grassland habitats. Breeding densities of godwits were recorded over 2 years, comparing lighted and unlighted con- ditions near a roadway and near light poles installed in a wet grassland away from the road influence. When all other habitat fac- tors were taken into account, the density of nests was slightly but statistically lower up to 300 m away from the lighting at roadway and control sites. The researchers also noted that birds nesting earlier in the year chose sites farther away from the lighting, while those nesting later filled in sites closer to the lights. Communication Visual communication within and between species may be influenced by artificial night lighting. Some species use light to communi- cate, and are therefore especially susceptible to disruption. Female glow-worms attract males up to 45 m away with bioluminescent flashes; the presence of artificial lighting reduces the visibility of these communi- cations. Similarly, the complex visual communication system of fireflies could be impaired by stray light (Lloyd 1994). Artificial night lighting could also alter communication patterns as a secondary effect. Coyotes (Canis latrans) group howl and group yip-howl more during the new moon, when it is darkest. Communication is necessary either to reduce trespassing from other packs, or to assem- ble packs to hunt larger prey during dark conditions (Bender et al. 1996). Sky glow could increase ambient illu- mination to eliminate this pattern in affected areas. Because of the central role of vision in orientation and behavior of most animals, it is not surprising that artificial lighting alters behavior. This causes an immediate conser- vation concern for some species, while for other species the influence may seem to be positive. Such “positive” effects, however, may have negative consequences within the context of community ecology. Community ecology The behaviors exhibited by individual animals in response to ambient illumination (orientation, disorien- tation) and to luminance (attraction, repulsion) influ- ence community interactions, of which competition and predation are examples. Competition Artificial night lighting could disrupt the interactions of groups of species that show resource partitioning across illumination gradients. For example, in natural commu- nities, some foraging times are partitioned among species that prefer different levels of lighting. The squirrel treefrog (Hyla squirrela) is able to orient and forage at lighting levels as low as 10-5 lux and under natural condi- tions typically will stop foraging at illuminations above 10-3 lux (Buchanan 1998). The western toad (Bufo boreas) forages only at illuminations between 10-1 and 10-5 lux, while the tailed frog (Ascaphus truei) forages only Ecological light pollution T Longcore and C Rich tion is a central topic for research on small mammals, rep- tiles, and birds (Kotler 1984; Lima 1998). Small rodents forage less at high illumination levels (Lima 1998), a ten- dency also exhibited by some lagomorphs (Gilbert and Boutin 1991), marsupials (Laferrier 1997), snakes (Klauber 1939), bats (Rydell 1992), fish (Gibson 1978), aquatic invertebrates (Moore et al. 2000), and other taxa. Unexpected changes in light conditions may disrupt predator–prey relationships. Gliwicz (1986, 1999) des- cribes high predation by fish on zooplankton during nights when the full moon rose hours after sunset. Zooplankton had migrated to the surface to forage under cover of dark- ness, only to be illuminated by the rising moon and sub- jected to intense predation. This “lunar light trap” (Gliwicz 1986) illustrates a natural occurrence, but unex- pected illumination from human sources could disrupt predator–prey interactions in a similar manner, often to the benefit of the predator. Available research shows that artificial night lighting disrupts predator–prey relationships, which is consistent with the documented importance of natural light regimes in mediating such interactions. In one example, harbor seals (Phoca vitulina) congregated under artificial lights to eat juvenile salmonids as they migrated downstream; turn- ing the lights off reduced predation levels (Yurk and Trites 2000). Nighttime illumination at urban crow roosts was higher than at control sites, presumably because this helps the crows avoid predation from owls (Gorenzel and Salmon 1995). Desert rodents reduced foraging activity when exposed to the light of a single camp lantern (Kotler 1984). Frank (1988) reviews predation by bats, birds, skunks, toads, and spiders on moths attracted to artificial lights. Mercury vapor lights, in particular, disrupt the interaction between bats and tympanate moths by inter- fering with moth detection of ultrasonic chirps used by bats in echolocation, leaving moths unable to take their normal evasive action (Svensson and Rydell 1998). From these examples, it follows that community struc- ture will be altered where light affects interspecific inter- actions. A “perpetual full moon” from artificial lights will favor light-tolerant species and exclude others. If the dark- est natural conditions never occur, those species that max- imize foraging during the new moon could eventually be compromised, at risk of failing to meet monthly energy budgets. The resulting community structure would be sim- plified, and these changes could in turn affect ecosystem characteristics. Ecosystem effects The cumulative effects of behavioral changes induced by artificial night lighting on competition and predation have the potential to disrupt key ecosystem functions. The spillover effects from ecological light pollution on aquatic invertebrates illustrates this point. Many aquatic invertebrates, such as zooplankton, move up and down within the water column during a 24-hour period, in a behavior known as “diel vertical migration”. Diel vertical migration presumably results from a need to avoid preda- tion during lighted conditions, so many zooplankton for- age near water surfaces only during dark conditions T Longcore and C Rich Ecological light pollution engineers to improve equipment to measure light charac- teristics at ecologically relevant levels under diverse field conditions. Researchers should give special considera- tion to the tropics, where the constancy of day–night lighting patterns has probably resulted in narrow niche breadths relative to illumination. Aquatic ecosystems deserve increased attention as well, because despite the central importance of light to freshwater and marine ecology, consideration of artificial lighting has so far been limited. Research on the effects of artificial night lighting will enhance understanding of urban ecosystems – the two National Science Foundation (NSF) urban Long Term Ecological Research sites are ideal locations for such efforts. Careful research focusing on artificial night lighting will probably reveal it to be a powerful force structuring local communities by disrupting competition and predator–prey interactions. Researchers will face the challenge of disen- tangling the confounding and cumulative effects of other facets of human disturbance with which artificial night lighting will often be correlated, such as roads, urban development, noise, exotic species, animal harvest, and resource extraction. To do so, measurements of light dis- turbance should be included routinely as part of environ- mental monitoring protocols, such as the NSF’s National Ecological Observatory Network (NEON). Future research is likely to reveal artificial night lighting to be an important, independent, and cumulative factor in the dis- ruption of natural ecosystems, and a major challenge for their preservation. Ecologists have studied diel and lunar patterns in the behavior of organisms for the greater part of a century (see Park 1940 and references therein), and the deaths of birds from lights for nearly as long (Squires and Hanson 1918). Humans have now so altered the natural patterns of light and dark that these new conditions must be afforded a more central role in research on species and ecosystems beyond the instances that leave carcasses on the ground. Acknowledgements We thank PJ DeVries for his photographs, and B Tuttle and C Elvidge for the satellite image. Research was sup- ported in part by the Conservation and Research Foundation. We are grateful for constructive comments and advice from W Briggs, BW Buchanan, KD Frank, JE Lloyd, JR Longcore, MV Moore, WA Montevecchi, G Perry, and M Salmon. References Beier P. 1995. Dispersal of juvenile cougars in fragmented habitat. J Wildlife Manage 59: 228–37. Bender DJ, Bayne EM, and Brigham RM. 1996. Lunar condition influences coyote (Canis latrans) howling. Am Midl Nat 136: 413–17. Bergen F and Abs M. 1997. Etho-ecological study of the singing activity of the blue tit (Parus caeruleus), great tit (Parus major) and chaffinch (Fringilla coelebs). J Ornithol 138: 451–67. Blake D, Hutson AM, Racey PA, et al. 1994. Use of lamplit roads by foraging bats in southern England. J Zool 234: 453–62. Buchanan BW. 1993. Effects of enhanced lighting on the behav- iour of nocturnal frogs. Anim Behav 45: 893–99. Ecological light pollution T Longcore and C Rich Kolligs D. 2000. Ökologische Auswirkungen künstlicher Lichtquellen auf nachtaktive Insekten, insbesondere Schmetterlinge (Lepidoptera) [Ecological effects of artificial light sources on noc- turnally active insects, in particular on moths (Lepidoptera)]. Faunistisch-Ökologische Mitteilungen Suppl 28: 1–136. Kotler BP. 1984. Risk of predation and the structure of desert rodent communities. Ecology 65: 689–701. Laferrier J. 1997. The influence of moonlight on activity of wooly opossums (Caluromys philander). J Mammal 78: 251–55. Lima SL. 1998. Stress and decision-making under the risk of preda- tion: recent developments from behavioral, reproductive, and ecological perspectives. Adv Stud Behav 27: 215–90. Lloyd JE. 1994. Where are the lightningbugs? Fireflyer Companion 1: 1, 2, 5, 10. Long WJ. 1901. Wilderness ways. Boston, MA: Ginn and Company. Moore MV, Pierce SM, Walsh HM, et al. 2000. Urban light pollu- tion alters the diel vertical migration of Daphnia. Verh Internat Verein Limnol 27: 779–82. Munday PL, Jones GP, Ohman MC, and Kaly UL. 1998. Enhancement of recruitment to coral reefs using light-attrac- tors. B Mar Sci 63: 581–88. Nakamura T and Yamashita S. 1997. Phototactic behavior of noc- turnal and diurnal spiders: negative and positive phototaxes. Zool Sci 14: 199–203. Ogden LJE. 1996. Collision course: the hazards of lighted structures and windows to migrating birds. Toronto, Canada: World Wildlife Fund Canada and Fatal Light Awareness Program. Outen AR. 2002. The ecological effects of road lighting. In: Sherwood B, Culter D, and Burton JA (Eds). Wildlife and roads: the ecological impact. London, UK: Imperial College Press. Park O. 1940. Nocturnalism — the development of a problem. Ecol Monogr 10: 485–536. Rand AS, Bridarolli ME, Dries L, and Ryan MJ. 1997. Light levels influence female choice in Tungara frogs: predation risk assess- ment? Copeia 1997: 447–50. Riiters KH and Wickham JD. 2003. How far to the nearest road? Front Ecol Environ 1: 125–29. Rydell J. 1992. Exploitation of insects around streetlamps by bats in Sweden. Funct Ecol 6: 744–50. Rydell J and Baagøe HJ. 1996. Gatlampor ökar fladdermössens pre- dation på fjärilar [Streetlamps increase bat predation on moths]. Entomol Tidskr 117: 129–35. Salmon M. 2003. Artificial night lighting and sea turtles. Biologist 50: 163–68. Salmon M, Tolbert MG, Painter DP, et al. 1995. Behavior of logger- head sea turtles on an urban beach. II. Hatchling orientation. J Herpetol 29: 568–76. Schmiedel J. 2001. Auswirkungen künstlicher Beleuchtung auf die Tierwelt – ein Überblick [Effects of artificial lighting on the animal world – an overview]. Schriftenreihe Landschaftspflege und Naturschutz 67: 19–51. Schwartz A and Henderson RW. 1991. Amphibians and reptiles of the West Indies: descriptions, distributions, and natural history. Gainesville, FL: University of Florida Press. Squires WA and Hanson HE. 1918. The destruction of birds at the lighthouses on the coast of California. Condor 20: 6–10. Summers CG. 1997. Phototactic behavior of Bemisia argentifolii (Homoptera: Aleyrodidae) crawlers. Ann Entomol Soc Am 90: 3.8.7.6 - Conversion of Static Billboards (A) Generally. The purpose of this Section is to provide an incentive for the reduction of sign clutter by allowing for digital billboard replacements (DBR) in exchange for the removal of existing static billboards according to the provisions set out herein. The digital electronic message center components of DBR are exempt from § 3.8.7.1(J)(2), and instead are subject to the standards of this Section. DBR are subject to all other provisions of § 3.8.7.1. (B) Replacement Ratio. The applicant for a (DBR) shall provide proof that the greater of eight static sign faces or 2,200 sf. of static sign face from existing billboards within the City of Fort Collins or its Growth Management Area will be permanently removed for each sign face that is the subject of a DBR. Where a sign structure remains with no sign faces, the sign structure shall also be permanently removed. Existing static (C) Review Procedures. (1) An application to convert nonconforming billboards will undergo a Type 2 review. (D) Replacement Locations. (1) Within the City there shall not be more than five new DBR locations and shall be: (a) Prohibited in the D; R-L; R-C; P-O-L; U-E; R-U-L; R-F; N-C-L; and N-C-M zone districts. (b) Not located within 500 ft. from designated historic districts or landmarks, natural areas or parks, or property that is used or zoned for single-family, duplex, or single family attached residential uses (c) Not located within 1/8 mile (660 ft.) of Downtown Zone District (d) A location may contain a maximum of two DBR sign sides, each side is subject to the replacement ratio (2) DBR shall be separated from each other by not less than two miles, measured in a straight line between the DBR and existing static billboards. (3) DBR shall be set back as follows: (a) From public rights-of-way: 25 ft. (b) From traffic control devices and driver decision and action points: 100 ft. (d) From interchanges with limited access highways: 1,000 ft. (E) Sign Face Area. No sign face of a digital billboard replacement shall exceed 90 sf. in area, unless it is directed at an interstate highway, in which case it shall not exceed 250 sf. in area. (F) Sign Height. No DBR shall exceed 18 ft. in height, unless it is directed at an interstate highway, in which case it shall not exceed 26 ft. in height. If flush mounted on a side of a building it cannot cover any fenestration and not exceed the lesser of the roof line or 18 ft. in height. (G) Design and Operation. With respect to digital billboard replacements: (1) EMCs shall not have a pixel pitch that is greater than 16 mm. (2) The message displayed on an EMC shall be a single message (not a split screen with more than one message), which shall not change more frequently than set out in Table (F), Dwell Time. DRAFT PROPOSED CODE LANGUAGE ATTACHMENT 8 DRAFT DIGITAL BILLBOARD REGULATIONS (Legal Review Pending) CITY OF FORT COLLINS, COLORADO Table (F) Dwell Time Speed limit of street that EMC faces Minimum dwell time 50 m.p.h. or less 60 seconds More than 50 m.p.h. 24 seconds (3) DBR shall contain static messages only, and animated, dissolve, or fade transitions are not allowed. (4) DBR shall be controlled by dimming software and sensors to adjust brightness for nighttime viewing and variations in ambient light. The intensity of the light source shall not produce glare, the effect of which constitutes a traffic hazard or is otherwise detrimental to the public health, safety or welfare. (5) DBR that are mounted on poles shall utilize pole covers to hide the poles from view. (H) Certification. Prior to acceptance of the installation by the City, the permit holder shall schedule and inspection with a Zoning Inspector to verify compliance. The permit holder and the business owner, business manager or property manager shall be in attendance during the inspection. Definitions 5.1.2 - Definitions.0F 1 Digital billboard means a billboard that incorporates an electronic message center on at least one sign face. Digital billboard replacement means the replacement of all or part of a static sign face of an existing billboard with a digital electronic message center or a new billboard with an electronic message center that replaces existing billboards without electronic message centers, according to the provisions of § 3.8.7.6. 1 These definitions should be alphabetically interleaved with the existing definitions in Section 5.1.2 of the Land Use Code. ATTACHMENT 8 February 26, 2019 Digital Billboard Regulations Noah Beals Senior City Planner - Zoning ATTACHMENT 9 Objective 2 1. Does Council want to proceed with consideration of an ordinance introducing Digital Billboard regulations? Phase 2 Sign Code Update 3 1. Phase 2 Sign Code Update was approved in December of 2018 2. Digital Billboard Regulations were separated from the sign code update Existing Billboards 4 • Static Billboards exist throughout the City and within the Growth Management Area. • Current Regulations do not allow for new billboards or existing billboards to be altered. • Existing billboards are removed when a property owner chooses to do so. This typically occurs at time of redevelopment. Goals of Digital Billboard Regulations 5 • Accelerate reduction of the number of billboards throughout the city • Remove existing billboards from the downtown and along the Poudre River • Provide clear guidance where new billboards could be located and public process to review a request Digital Billboard Replacement 6 • There are 85 static billboard locations within the City and Growth Management Area (144 billboard faces) Digital Billboard Replacement 7 OPTIONS: • Keep current regulations, No new billboards • Allow new digital billboards • Allow new digital billboards with a replacement ratio of 8:1 • Allow new digital billboards with a replacement ratio of 6:1 Digital Billboard Replacement 8 Jurisdiction Replacement Ratio Other Requirments Nevada DOT Ratio 1:1 St. Petersburg Florida Ratio 14:1 Required to removal of 83 billboards for 6 Digital billboards Kalamazoo, MI Ratio 6:1 Maximum of 8 digital billboards Minnetonka, MI Ratio 2:1 Required only 15 of 30 billboards to be removed then allowed a maximum of 8 digital billboards San Antonio, TX Ratio 4:1 Sand Diego County Banned Digital Billboards Considered 3:1 Ratio Other Examples of a Replacement Ratio Digital Billboard Replacement 9 Option 8:1 Replacement Ratio Must Remove: 8 billboards or 2,200 sf whichever is greater. Allowed:1 digital billboard. Limited to 5 locations if all are double sided affects most of the 85 existing locations. Digital Billboard Replacement 10 Option 6:1 Replacement Ratio Must Remove: 6 billboards or 1,900 sf whichever is greater. Allowed:1 digital billboard. Limited to 5 locations if all are double sided affects 60 existing locations. Digital Billboard Replacement 11 Natural Resources Advisory Board (NRAB) Suggested prioritizing the removal of existing billboards that are illuminated by spot lights. One way to do this would be to allow a illuminated static billboard to count 1 = 1.5 billboards or 1sf = 1.5sf in the replacement ratio. Of the existing inventory there are 21 illuminated locations Objective 12 1. Does Council want to proceed with consideration of an ordinance introducing Digital Billboard regulations? Attachment 6 Attachment 7 Community Development & Neighborhood Services 281 North College Avenue P.O. Box 580 Fort Collins, CO 80522.0580 970.416.2740 970.224.6134- fax fcgov.com Planning, Development & Transportation Services MEMORANDUM DATE: February 4, 2019 TO: Mayor Troxell and City Councilmembers THRU: Darin Atteberry, City Manager Jeff Mihelich, Deputy City Manager Laurie Kadrich, Planning, Development & Transportation Director Tom Leeson, Community Development Neighborhood Services Director FROM: Noah Beals, City Sr. Planner RE: Information Requested Regarding Proposed Digital Billboard Standards Background: The purpose of this memo is to provide City Council with requested information concerning the proposed digital billboard standards. We are seeking to know if City Council would like another work session on Digital Billboard code language or to proceed to a regular meeting with an ordinance of proposed standards. These requests, which are listed below, are from the Aug. 14, 2018 work session where staff presented the draft of the proposed Sign Code update that included a section that presented an option to convert existing static billboards in both the City and Growth Management Area to a limited number of digital billboard locations. Council requests: • Council requested exact information on the number, location and design details of the existing static billboards. Attached to this memo is a map that illustrates the existing billboards. The total number of existing billboards is 144, within 85 different locations. Of the 144 billboards 35% are greater than 90 sq. ft. in size. • Council requested data on safety issues related to digital billboards and the proposed standards. DocuSign Envelope ID: 58D2B94B-A480-4E77-882A-EA0A02A62BE2 - 2 - LAMAR advertising company sent an email that was sent to the Mayor and Council members on Sept 7th , 2018 that included a report on digital billboards and an article on safety studies. The report was commissioned by the Federal Highway Administration and presented three conclusions. o Conclusion: Commercial electronic variable message signs (CEVMS) do not appear to be related to a decrease in looking toward the road ahead. o Conclusion: Driver view time of a CEVMS did not increase compared to view time of a static billboard and the view time is found to be within the acceptable threshold of National Highway Traffic Safety Administration (NHTSA). o Conclusion: The study added to the knowledge base on the issues examined but did not present definitive answers to the research question investigated. In a publication of the American Planning Association, Zoning Practice Smart Sign Codes the finding of several studies was reported as follows: o 2006 Study by the National Highway Traffic Safety Administration focused on driver distractions and found any distraction of more than 2 seconds is a potential cause of crashes and near crashes. o 2004 study by the University of Toronto found that drivers make twice as many glances at video signs than they do at static signs and drivers’ glances at an active sign were longer in duration. o Prior to 2004, the University of Toronto found that drivers made the same number of glances at traffic signals and street signs with and without video billboards present. o 2005 study by Texas transportation found that flashing and changing messages are more distracting and require more reading time to comprehend the message o 2001 study commissioned by the City of Seattle concluded that electronic signs that moving/flash images distract drivers for longer intervals than electronic signs with no movement. This report recommended a 10 second message display time. With different reports and findings, the data does not reach a conclusive result. • Council requested examples of other jurisdictions that proposed similar approach to reducing billboards. The following are other jurisdictions conversion rates: o San Diego County considered two options:  3:1 ratio: remove 3 static billboards and place 1 electronic billboard  3:1 ratio in square footage, remove 600 sf of static billboards for 200 sf of electronic billboard DocuSign Envelope ID: 58D2B94B-A480-4E77-882A-EA0A02A62BE2 - 3 - After review and consideration San Diego County choose to not allow digital billboards. o Nevada Department of Transportation does not have a ratio. They allow the conversion to take place if it meets all other state and local standards. o St. Petersburg, Florida agreed with a Billboard company to remove 83 static billboards for the 6 new digital billboards (14:1 ratio). o Kalamazoo, MI currently drafting an ordinance with a 6:1 ratio with a maximum of 8 digital billboards o Minnetonka, MI agreed with a billboard company to the removal of 15 out of 30 static billboards in exchange for 8 digital billboards (almost a 2:1 ratio) o San Antonio, TX ordinance requires the removal of 4 static billboards in exchange for 1 digital billboard. • Council requested the analysis on the proposed 8:1 ratio recommended by staff and the consultant: In proposing a ratio for a billboard exchange the goal is to reduce the number of existing billboards as much as possible. The proposed 8:1 ratio affects the majority of existing 85 billboard locations. The first approach suggested a setback distance for a digital billboard from any other billboard (existing or new). The thought was the setback would eliminate most however we found the following: o 10 Digital Billboards with 2 mile spacing, removes 71 sign structures (2mi radius): 6 Posters, 21 Junior Posters, 44 Bulletins o 8 Digital Billboards with 3 mile spacing, removes 75 sign structures (2mi radius): 6 Posters, 21 Junior Posters, 48 Bulletins o 6 Digital Billboards with 4 mile spacing, removes 51 signs (2mi radius) Additionally, this approach assumed a new digital billboard could be in the same area of most of the existing billboards. This assumption is not accurate based on State and Federal restrictions along Mulberry and parts of I-25 The conclusion was made that a 2-mile separation from another billboard with the required number and square footage reduction would provide greater flexibility for the applicant in deciding which to remove while ensuring the removal of existing static billboards. • Council requested the analysis on the proposed message display time recommended by staff and the consultant: DocuSign Envelope ID: 58D2B94B-A480-4E77-882A-EA0A02A62BE2 - 4 - We looked at the display times of other jurisdictions and found the following: City Message change delay time City Message change delay time Greeley 30 seconds Thornton 5 seconds Loveland 5 seconds Longmont 60 seconds Lakewood 8 seconds Provo 3 times a day/ High churn 8 seconds Boulder 60 seconds Gainesville Electronic signs Prohibited Westminster 30 minutes Denton None Arvada 8 seconds Burbank Electronic signs Prohibited Fort Collins Current 60 seconds Fort Collins Proposed 24 seconds along roads with speed limits of 55MPH and 60 seconds along all other roads While most communities allow a display time of less than 60 seconds, a 60 second display time is not unprecedented. The intent of a longer display time addresses both the safety and aesthetics of digital signs. The Land Use Code prohibits flashing/blinking signs, and the goal is to strike a balance between the aesthetics of a flashing/blinking sign and a static sign. Colorado Department of Transportation requires a four second dwell time. Typical add space is sold in 8 to 10 second intervals. Working with the 8 second interval, 16 seconds provided a greater number of changes increasing the driver distraction. A 24 second display time provides more of a static appearance along the I-25 and addresses both safety and aesthetics of signs. While the 24 second display time is less than 60 seconds, reducing the delay time would be acceptable because at higher speeds the time a sign is viewed is reduced. Again, it is a balance between creating an appearance of a flashing/blinking sign and a static sign. • Council requested the digital billboard standards be presented to the Natural Resources Advisory Board (NRAB). On September 19, 2018 the NRAB voted in support of the proposed digital billboard regulations. Additionally, they provided the following points: o As static signs are being considered for replacement those with existing illumination should be of the highest priority for removal. o We would like to see language within the code that makes it illegal to have signs face into any natural area. o We would discourage the placement of digital signs in our gateway areas (Harmony Rd., Prospect Rd., and Mulberry Rd.) One of the ideas brought up by NRAB that has not been discussed yet is the idea of incentivizing the removal of static billboards that already are luminated. We plan to further explore this idea. DocuSign Envelope ID: 58D2B94B-A480-4E77-882A-EA0A02A62BE2 - 5 - In addition to presenting to the NRAB we discussed the proposed digital billboard standards with the city’s internal Night Sky team. This team discussed the following topics: o Brightness levels between sunset and sunrise o Color temperature at night (blue + white spectrum light) o Dwell times for changing messages o Exchange of static billboards for Digital Billboards o Safety (distraction + glare) These discussions focused on safety for both human and animals. Additionally, studies on the effects of lighting at night were provided (see attached). Overall, the preference is for no artificial light. However, if artificial light is necessary than it was suggested that it be adjustable to blend with the ambient light levels. • Council Requested information on the Return on Investment (ROI) for an applicant pursing a new digital billboard. The City requested such information from LAMAR, the owner of the majority of billboards in Fort Collins. The response from LAMAR discussed previous ratio replacements they conducted in other jurisdictions that have all been 3:1 or less. LAMAR indicated these have worked well and concluded that a 4:1 ratio would work for their goals. However, they do not see how an 8:1 ratio would be economically viable. Additionally, we asked if a 6:1 ratio would work, and while they are appreciative of a ratio less than 8:1, they cannot guarantee that any more than a 4:1 ratio is viable. No empirical data was provided. Conclusion: We are seeking to know if City Council would like another work session on Digital Billboard code language or to proceed to a regular meeting with an ordinance of proposed standards. Attachments: • Existing Billboard Location Map DocuSign Envelope ID: 58D2B94B-A480-4E77-882A-EA0A02A62BE2 INTERSTATE 25 S SHIELDS ST S COLLEGE AVE S TAFT HILL RD E VINE DR S TIMBERLINE RD LAPORTE AVE S LEMAY AVE E PROSPECT RD E DOUGLAS RD E TRILBY RD W DRAKE RD E HARMONY RD N OVERLAND TRL E DRAKE RD W TRILBY RD W PROSPECT RD N SHIELDS ST E MULBERRY ST COUNTY ROAD 54G STATE HIGHWAY 392 W MULBERRY ST SE FRONTAGE RD S OVERLAND TRL SW FRONTAGE RD E COUNTY ROAD 30 S COUNTY ROAD 5 STOVER ST W OAK ST NE FRONTAGE RD E LINCOLN AVE RIVERSIDE AVE CARPENTER RD W COUNTY ROAD 38E W ELIZABETH ST S COUNTY ROAD 23 BAY RD RICHARDS LAKE RD N US HIGHWAY 287 N TAFT HILL RD W HORSETOOTH RD W HARMONY RD TURNBERRY RD N COLLEGE AVE MAIN ST WHEDBEE ST E STUART ST COUNTRY CLUB RD HIDDEN SPRINGS RD N LEMAY AVE BIGHORN XING W SWALLOW RD SMITH ST S COUNTY ROAD 19 W MOUNTAIN AVE S MASON ST TERRY LAKE RD PETERSON ST SENECA ST MAX GUIDEWAY MOUNTAIN VISTA DR BUSCH DR LADY MOON DR W PLUM ST W VINE DR BINGHAM HILL RD ELM ST W LAKE ST ZEPHYR RD TILDEN ST STRAUSS CABIN RD S SUMMIT VIEW DR E PITKIN ST N TIMBERLINE RD N COUNTY ROAD 5 W STUART ST S CENTENNIAL DR ZIEGLER RD CONIFER ST DUNBAR AVE FRONTAGE RD E WILLOX LN WOOD ST GREGORY RD TURMAN DR CUSTER DR E ELIZABETH ST CENTRE AVE GIDDINGS RD W WILLOX LN CORBETT DR MCMURRY AVE E COUNTY ROAD 38 COLUMBIA RD ROCK CREEK DR WHEATON DR HAMPSHIRE RD PINE WABASH ST KECHTER RD MINUTEMAN DR BUCKSKIN TRL WELCH ST CHASE DR FOSSIL CREEK PKWY TULANE DR CLYDE ST CINQUEFOIL LN RIM ROCK TRL CARIBOU DR OAKRIDGE DR LINDEN LAKE RD LINDEN ST SUNSTONE DR W DOUGLAS RD ANTELOPE RD SHORE RD SPRINGFIELD DR MICHAUD LN YORKSHIRE ST S COUNTY ROAD 3F MCCLELLAND DR E COUNTY ROAD 36 MIDPOINT DR 12TH ST SPRING MESA RD BRITTANY DR CITY PARK AVE MAPLE ST AVONDALE RD WESTRIDGE DR S COUNTY ROAD 9 N COUNTY ROAD 23 S US HIGHWAY 287 NW FRONTAGE RD N COUNTY ROAD 9 SPRING CANYON RANCH RD SHORELINE DR E HORSETOOTH RD MEADOWLARK AVE W PITKIN ST E COUNTY ROAD 50 E COUNTY ROAD 48 EAST DR S COUNTY ROAD 11 FOSSIL CREEK DR 9TH ST RAMPART RD DUFF DR LYNDA LN N COUNTY ROAD 17 KYLE AVE MARIAH LN BOLTZ DR WESTGATE DR STANFORD RD JOHN F KENNEDY PKWY LAWTON LN ROYAL DR PARKER ST S HOWES ST SYKES DR SKYLINE DR LA EDA LN E LOCUST ST MATHEWS ST RANGER DR KIRKWOOD DR ELGIN CT N SUNSET ST E OLIVE ST BRUNS DR CENTENNIAL RD E COUNTY ROAD 54 ANN ST S GRANT AVE WILD VIEW DR COUNTY ROAD 42C REGENCY DR HULL ST E SKYWAY DR LINDENWOOD DR E OAK ST HUGHES WAY E COUNTY ROAD 52 ELM EAGLE DR IRISH DR COLONY DR HINSDALE DR E LAKE ST PLATTE DR BAR HARBOR DR N LINK LN MCLAUGHLIN LN CREST RD E COUNTY ROAD 32E BLUE SPRUCE DR STRACHAN DR TYLER ST HOGAN DR N GRANT AVE KEENLAND DR REMINGTON ST ARROWHEAD RD WATERGLEN DR RED FOX RD AIRPARK DR HILLSIDE DR E SATURN DR OLD MILL RD GOODELL LN BREAKWATER DR LIMON DR CANAL DR S LINK LN HEMLOCK ST WAKONDA DR POST RD MARS DR THOREAU DR PARKLAKE DR HAVEL AVE 1ST ST RAWHIDE DR OHIO IOWA ABBOTSFORD ST SHALLOW POND DR GARFIELD ST VALLEY FORGE AVE DENVER DR SNOW MESA DR MANHATTAN AVE ZURICH DR VICOT WAY PEYTON DR N HOLLYWOOD ST LANDINGS DR SPAULDING LN OFF RAMP S COUNTY ROAD 7 SHEELY DR EASTWOOD DR IDLEDALE DR RULE DR JOSEPH ALLEN DR WEITZEL ST JAY DR HEARTHFIRE DR FORECASTLE DR HEADWATER DR APPLE DR SHARP POINT DR THOREAU RD MOORE LN PECAN ST CAMINO REAL TICONDEROGA DR BRYCE DR WINDOM ST KITCHELL WAY ALLOTT AVE CAJETAN ST N IMPALA DR ACADEMY CT FALL HARVEST WAY LYNN DR YEARLING DR EDITH DR VICTORIA DR RED OAK CT LE FEVER DR S BRYAN AVE N MASON ST CASA LN WHITE WILLOW DR HOFFMAN MILL RD FLEET DR MACKINAC ST E COUNTY ROAD 40 TAMARISK DR SCENIC DR SHIRE CT CARMICHAEL ST WAPITI RD CAMPFIRE DR ON RAMP INTERNATIONAL BLVD LUKE ST N COUNTY ROAD 23E FAIRWAY LN WHALERS WAY MCHUGH ST STREAMSIDE DR WOOD LN SILVERGATE RD ROSS DR TRUXTUN DR DIXON COVE DR BUTTE PASS DR HILLDALE DR CLOVERLEAF WAY SNOWY PLAIN RD MIDWAY DR BISON RD HEPPLEWHITE CT MERGANSER DR LOWELL LN WILMINGTON DR HOLYOKE CT THARP DR DAKOTA ST BOXELDER DR CINDY LN SMOKEY ST PARK PLACE DR BRENTON DR FIELDSTONE DR BIPLANE ST WINFIELD DR QUEST DR DELANY DR ENFIELD ST LAREDO LN TERRY RIDGE RD BUCHANAN ST WAYNE ST CHESAPEAKE DR ROOKERY RD PARKSIDE DR CARRIAGE RD KITTERY CT PAWNEE DR ORILLA DEL LAGO SHILO DR COVENTRY CT EASTBROOK DR PAVILION LN TRESTLE RD BUCKEYE ST SILVER MIST LN WEBSTER AVE CATALINA DR ESSEX DR DAVIDSON DR BOXELDER ST PRADO DR NEIL DR ALCOTT ST S SHERWOOD ST KRISRON RD ORBIT WAY MEADOW LN MEDLAR PL SUNRISE DR BIANCO DR NELSON LN 11TH ST ERIC ST KINGSTON DR BAYSIDE DR BRUMBY LN HARVEST PARK LN RELIANT ST WINAMAC DR DONELLA CT LOWE ST SUNRISE LN CATALPA DR SAN LUIS ST QUEENS CT CHARLIE LN MARBLE DR SHANNON DR CABOT CT BOONE ST LYMEN ST WINGFOOT DR VINSON ST MERCY DR TEAL DR DAYTON DR BATELEUR LN DEER TRAIL CT CHERRY ST S MASON ST FRONTAGE RD OFF RAMP MOORE LN MAX GUIDEWAY S LEMAY AVE OFF RAMP E COUNTY ROAD 30 W LAKE ST W LAKE ST STOVER ST SW FRONTAGE RD W VINE DR S COUNTY ROAD 5 OFF RAMP INTERSTATE 25 E DRAKE RD SE FRONTAGE RD MAIN ST S TIMBERLINE RD TURNBERRY RD MAPLE ST ELM ST OFF RAMP ON RAMP Legend Downtown Zone City of Fort Collins City Limits Growth Management Area !( Billboard Sign Location ¯ 0 0.5 1 1.5 2 2.5 3 Miles DocuSign Envelope ID: 58D2B94B-A480-4E77-882A-EA0A02A62BE2 372–79. Svensson AM and Rydell J. 1998. Mercury vapour lamps interfere with the bat defence of tympanate moths (Operophtera spp; Geometridae). Anim Behav 55: 223–26. Verheijen FJ. 1985. Photopollution: artificial light optic spatial control systems fail to cope with. Incidents, causations, reme- dies. Exp Biol 44: 1–18. Wiese FK, Montevecchi WA, Davoren GK, et al. 2001. Seabirds at risk around offshore oil platforms in the North-west Atlantic. Mar Pollut Bull 42: 1285–90. Witherington BE. 1997. The problem of photopollution for sea tur- tles and other nocturnal animals. In: Clemmons JR and Buchholz R (Eds). Behavioral approaches to conservation in the wild. Cambridge, UK: Cambridge University Press. Yurk H and Trites AW. 2000. Experimental attempts to reduce pre- dation by harbor seals on out-migrating juvenile salmonids. Trans Am Fish Soc 129: 1360–66. 198 www.frontiersinecology.org © The Ecological Society of America Buchanan BW. 1998. Low-illumination prey detection by squirrel treefrogs. J Herpetol 32: 270–74. Cinzano P, Falchi F, and Elvidge CD. 2001. The first world atlas of the artificial night sky brightness. Mon Not R Astron Soc 328: 689–707. De Molenaar JG, Jonkers DA, and Sanders ME. 2000. Road illumi- nation and nature. III. Local influence of road lights on a black-tailed godwit (Limosa l. limosa) population. Wageningen, The Netherlands: Alterra. Derrickson KC. 1988. Variation in repertoire presentation in northern mockingbirds. Condor 90: 592–606. Dick MH and Donaldson W. 1978. Fishing vessel endangered by crested auklet landings. Condor 80: 235–36. Dodson S. 1990. Predicting diel vertical migration of zooplankton. Limnol and Oceanogr 35: 1195–1200. Eisenbeis G and Hassel F. 2000. Zur Anziehung nachtaktiver Insekten durch Straßenlaternen – eine Studie kommunaler Beleuchtungseinrichtungen in der Agrarlandschaft Rein- hessens [Attraction of nocturnal insects to street lights – a study of municipal lighting systems in a rural area of Rheinhessen (Germany)]. Natur und Landschaft 75: 145–56. Elvidge C, Baugh KE, Kihn EA, and Davis ER. 1997. Mapping city lights with nighttime data from the DMSP Operational Linescan System. Photogramm Eng Rem S 63: 727–34. Elvidge CD, Imhoff ML, Baugh KE, et al. 2001. Nighttime lights of the world: 1994–95. ISPRS J Photogramm Rem S 56: 81–99. Frank KD. 1988. Impact of outdoor lighting on moths: an assess- ment. J Lepidop Soc 42: 63–93. Gal G, Loew ER, Rudstam LG, and Mohammadian AM. 1999. Light and diel vertical migration: spectral sensitivity and light avoidance by Mysis relicta. Can J Fish Aquat Sci 56: 311–22. Gibson RN. 1978. Lunar and tidal rhythms in fish. In: Thorpe JE (Ed). Rhythmic activity of fishes. London: Academic Press. Gilbert BS and Boutin S. 1991. Effect of moonlight on winter activity of snowshoe hares. Arctic Alpine Res 23: 61–65. Gliwicz ZM. 1986. A lunar cycle in zooplankton. Ecology 67: 883–97. Gliwicz ZM. 1999. Predictability of seasonal and diel events in tropical and temperate lakes and reservoirs. In: Tundisi JG, Straskraba M (Eds). Theoretical reservoir ecology and its appli- cations. São Carlos: International Institute of Ecology. Gorenzel WP and Salmon TP. 1995. Characteristics of American Crow urban roosts in California. J Wildlife Manage 59: 638–45. Gotthard K. 2000. Increased risk of predation as a cost of high growth rate: an experimental test in a butterfly. J Anim Ecol 69: 896–902. Hailman JP. 1984. Bimodal nocturnal activity of the western toad (Bufo boreas) in relation to ambient illumination. Copeia 1984: 283–90. Haymes GT, Patrick PH, and Onisto LJ. 1984. Attraction of fish to mercury vapor light and its application in a generating station forebay. Int Rev Hydrobiol 69: 867–76. Health Council of the Netherlands. 2000. Impact of outdoor light- ing on man and nature. The Hague: Health Council of the Netherlands. Publication No. 2000/25E. Hill D. 1990. The impact of noise and artificial light on waterfowl behaviour: a review and synthesis of the available literature. Norfolk, United Kingdom: British Trust for Ornithology Report No. 61. Jaeger RG and Hailman JP. 1973. Effects of intensity on the photo- tactic responses of adult anuran amphibians: a comparative sur- vey. Z Tierpsychol 33: 352–407. Klauber LM. 1939. Rattlesnakes: their habits, life histories, and influence on mankind. Berkeley, CA: University of California Press. 197 © The Ecological Society of America www.frontiersinecology.org (Gliwicz 1986). Light dimmer than that of a half moon (<10-1 lux) is sufficient to influence the vertical distribu- tion of some aquatic invertebrates, and indeed patterns of diel vertical migration change with the lunar cycle (Dodson 1990). Moore et al. (2000) documented the effect of artificial light on the diel migration of the zooplankton Daphnia in the wild. Artificial illumination decreased the magnitude of diel migrations, both in the range of vertical movement and the number of individuals migrating. The researchers hypothesize that this disruption of diel vertical migration may have substantial detrimental effects on ecosystem health. With fewer zooplankton migrating to the surface to graze, algae populations may increase. Such algal blooms would then have a series of adverse effects on water quality (Moore et al. 2000). The reverberating effects of community changes caused by artificial night lighting could influence other ecosys- tem functions. Although the outcomes are not yet pre- dictable, and redundancy will buffer changes, indications are that light-influenced ecosystems will suffer from important changes attributable to artificial light alone and in combination with other disturbances. Even remote areas may be exposed to increased illumination from sky glow, but the most noticeable effects will occur in those areas where lights are close to natural habitats. This may be in wilderness where summer getaways are built, along the expanding front of suburbanization, near the wetlands and estuaries that are often the last open spaces in cities, or on the open ocean, where cruise ships, squid boats, and oil derricks light the night. Conclusions Our understanding of the full range of ecological conse- quences of artificial night lighting is still limited, and the field holds many opportunities for basic and applied research. Studies of natural populations are necessary to investigate hypotheses generated in the laboratory, evi- dence of lunar cycles in wild populations, and natural his- tory observations. If current trends continue, the influ- ence of stray light on ecosystems will expand in geographic scope and intensity. Today, 20% of the area of the coterminous US lies within 125 m of a road (Riiters and Wickham 2003). Lights follow roads, and the propor- tion of ecosystems uninfluenced by altered light regimes is decreasing. We believe that many ecologists have neglected to consider artificial night lighting as a relevant environmental factor, while conservationists have cer- tainly neglected to include the nighttime environment in reserve and corridor design. Successful investigation of ecological light pollution will require collaboration with physical scientists and 196 www.frontiersinecology.org © The Ecological Society of America during the darkest part of the night at below 10-5 lux (Hailman 1984). While these three species are not neces- sarily sympatric (ie inhabiting the same area), and differ in other niche dimensions, they illustrate the division of the light gradient by foragers. Many bat species are attracted to insects that congre- gate around light sources (Frank 1988). Although it may seem that this is a positive effect, the increased food concentration benefits only those species that exploit light sources and could therefore result in altered community structure. Faster-flying species of bats congregate around lights to feed on insects, but other, slower-flying species avoid lights (Blake et al. 1994; Rydell and Baagøe 1996). Changes in competitive communities occur as diurnal species move into the “night light niche” (Schwartz and Henderson 1991). This concept, as originally described, applies to reptiles, but easily extends to other taxa, such as spiders (Frank pers comm) and birds (Hill 1990; Figure 5). Predation Although it may seem beneficial for diurnal species to be able to forage longer under artificial lights, any gains from increased activity time can be offset by increased preda- tion risk (Gotthard 2000). The balance between gains from extended foraging time and risk of increased preda- 195 © The Ecological Society of America www.frontiersinecology.org Figure 5. Crowned hornbill (Tockus alboterminatus) hawking insects at a light at the Kibale Forest National Park, Uganda. Courtesy of PJ DeVries In birds, some evidence suggests that artificial night lighting affects the choice of nest site. De Molenaar et al. 194 www.frontiersinecology.org © The Ecological Society of America Figure 4. Thousands of mayflies carpet the ground around a security light at Millecoquins Point in Naubinway on the Upper Peninsula of Michigan. Courtesy of PJ DeVries Figure 3. Attraction of frogs to a candle set out on a small raft. Illustration by Charles Copeland of an experiment in northern Maine or Canada described by William J Long (1901). Twelve or fifteen bullfrogs (Rana catesbeiana) climbed on to the small raft before it flipped over. © The Ecological Society of America www.frontiersinecology.org Figure 2. Distribution of artificial lights visible from space. Produced using cloud-free portions of low-light imaging data acquired by the US Air Force Defense Meteorological Satellite Program Operational Linescan System. Four types of lights are identified: (1) human settlements – cities, towns, and villages (white), (2) fires – defined as ephemeral lights on land (red), (3) gas flares (green), and (4) heavily lit fishing boats (blue). See Elvidge et al. (2001) for details. Image, data processing, and descriptive text by the National Oceanic and Atmospheric Administration’s National Geophysical Data Center. lengths not visible to humans – future research on ecolog- ical light pollution should identify these responses and measure light accordingly. For example, Gal et al. (1999) calculated the response curve of mysid shrimp to light and reported illumination in lux adjusted for the spectral sensitivity of the species. Ecologists are faced with a practical difficulty when communicating information about light conditions. Lux is the standard used by nearly all lighting designers, light- ing engineers, and environmental regulators; communi- cation with them requires reporting in this unit. Yet the use of lux ignores biologically relevant information. High- pressure sodium lights, for instance, will attract moths because of the presence of ultraviolet wavelengths, while low-pressure sodium lights of the same intensity, but not producing ultraviolet light, will not (Rydell 1992). Nevertheless, we use lux here, both because of the need to communicate with applied professionals, and because of its current and past widespread usage. As this research field develops, however, measurements of radiation and spectrum relevant to the organisms in question should be used, even though lux will probably continue to be the preferred unit for communication with professionals in other disciplines. Ecologists also measure aspects of the light environ- ment other than absolute illumination levels. A sudden change in illumination is disruptive for some species (Buchanan 1993), so percent change in illumination, rate, or similar measures may be relevant. Ecologists may also measure luminance (ie brightness) of light sources that are visible to organisms. 192 www.frontiersinecology.org © The Ecological Society of America Figure 1. Diagram of ecological and astronomical light pollution. Astronomical light pollution reduces the number of visible stars Unshielded lights can cause both astronomical and ecological light pollution Tall, lighted structures are collision hazards Shielded lights reduce astronomical light pollution but may still cause ecological light pollution Sky glow from cities disrupts distant ecosystems Ecological light pollution Travis Longcore and Catherine Rich Ecologists have long studied the critical role of natural light in regulating species interactions, but, with limited exceptions, have not investigated the consequences of artificial night lighting. In the past century, the extent and intensity of artificial night lighting has increased such that it has substantial effects on the biology and ecology of species in the wild. We distinguish “astronomical light pollution”, which obscures the view of the night sky, from “ecological light pollution”, which alters natural light regimes in terrestrial and aquatic ecosystems. Some of the catastrophic consequences of light for certain taxonomic groups are well known, such as the deaths of migratory birds around tall lighted structures, and those of hatchling sea turtles disoriented by lights on their natal beaches. The more subtle influences of artificial night lighting on the behavior and community ecology of species are less well recognized, and constitute a new focus for research in ecology and a pressing conservation challenge. Front Ecol Environ 2004; 2(4): 191–198 The Urban Wildlands Group, PO Box 24020, Los Angeles, CA 90024-0020 (longcore@urbanwildlands.org) In a nutshell: • Ecological light pollution includes chronic or periodically increased illumination, unexpected changes in illumination, and direct glare • Animals can experience increased orientation or disorienta- tion from additional illumination and are attracted to or repulsed by glare, which affects foraging, reproduction, commu- nication, and other critical behaviors • Artificial light disrupts interspecific interactions evolved in natural patterns of light and dark, with serious implications for community ecology ATTACHMENT 7 “The public needs to know about the factors causing [light pollution], but research is not going at the pace it should,” Blask says. Susan Golden, distinguished professor at the Center for Research on Biological Clocks of Texas A&M University in College Station, Texas, agrees. She says, “Light pollution is still way down the list of important environ- mental issues needing study. That’s why it’s so hard to get funds to research the issue.” “The policy implications of unnecessary light at night are enormous,” says Stevens in reference to the health and energy rami- fications [for more on the energy impact of light pollution, see “Switch On the Night: Policies for Smarter Lighting,” p. A28 this issue]. “It is fully as important an issue as global warming.” Moreover, he says, artificial light is a ubiquitous environmental agent. “Almost everyone in modern society uses electric light to reduce the natural daily dark period by extending light into the evening or before sunrise in the morning,” he says. “On that basis, we are all exposed to electric light at night, whereas before electricity, and still in much of the developing world, people get twelve hours of dark whether they are asleep or not.” Sources believe that the meeting at the NIEHS in September 2006 was a promis- ing beginning for moving forward on the light pollution issue. “Ten years ago, scientists thought something was there, but couldn’t put a finger on it,” says Leslie Reinlib, a pro- gram director at the NIEHS who helped orga- nize the meeting. “Now we are really just at the tip of the iceberg, but we do have some- thing that’s scientific and can be measured.” The 23 participants at the NIEHS- sponsored meeting identified a research agenda for further study that included the func- tioning of the circadian clock, epidemiologic studies to define the artificial light exposure/ disease relationship, the role of melatonin in artificial light–induced disease, and develop- ment of interventions and treatments to reduce the impact of light pollution on disease. “It was a very significant meeting,” Brainard says. “It’s the first time the National Institutes of Health sponsored a broad multidisciplinary look at the light-environmental question with the intent of moving to the next step.” Ron Chepesiuk also used their NHS cohort to investigate the connection between artificial light, night work, and colorectal cancer. In the 4 June 2003 issue of the Journal of the National Cancer Institute, they reported that nurs- es who worked night shifts at least 3 times a month for 15 years or more had a 35% increased risk of colo rectal cancer. This is the first significant evidence so far linking night work and colorectal cancer, so it’s too early to draw conclusions about a causal associa- tion. “There is even less evidence about colo- rectal cancer and the larger subject of light pollution,” explains Stevens. “That does not mean there is no effect, but rather, there is not enough evidence to render a verdict at this time.” The research on the shift work/cancer relationship is not conclusive, but it was enough for the International Agency for Research on Cancer (IARC) to classify shift work as a probable human carcinogen in 2007. “The IARC didn’t definitely call night shift work a carcinogen,” Brainard says. “It’s still too soon to go there, but there is enough evidence to raise the flag. That’s why more research is still needed.” The Role of Melatonin Brainard and a growing number of research- ers believe that melatonin may be the key to understanding the shift work/breast cancer risk association. Melatonin, a hormone pro- duced by the pineal gland, is secreted at night and is known for helping to regulate the body’s biologic clock. Melatonin triggers a host of biologic activities, possibly including a noctur- nal reduction in the body’s production of estrogen. The body produces melatonin at night, and melatonin lev- els drop precipitously in the presence of artificial or natu- ral light. Numerous studies suggest that decreasing noc- turnal melatonin production levels increases an individu- al’s risk of developing can- cer. [For more information on melatonin, see “Benefits of Sunlight: A Bright Spot for Human Health,” EHP 116:A160–A167 (2008).] One groundbreak- ing study published in the 1 December 2005 issue of Cancer Research implicated melatonin deficiency in what the report authors called a rational biologic explanation for the increased breast can- cer risk in female night shift workers. The study involved female volunteers whose blood was collected under three different conditions: during daylight hours, dur- ing the night after 2 hours of complete darkness, and during the night after exposure to 90 minutes of artificial light. The blood was injected into human breast tumors that were transplanted into rats. The tumors infused with melatonin-deficient blood col- lected after exposure to light during the night were found to grow at the same speed as those infused with daytime blood. The blood col- lected after exposure to darkness slowed tumor growth. “We now know that light suppresses melatonin, but we are not saying it is the only risk factor,” says first author David Blask, a research scientist at the Bassett Healthcare Research Institute in Coopers town, New York. “But light is a risk factor that may explain [previously unexplainable phenom- ena]. So we need to seriously consider it.” The National Cancer Institute estimates that 1 in 8 women will be diagnosed with breast cancer at some time during her life. We can attribute only about half of all breast cancer cases to known risk factors, says Brainard. Meanwhile, he says, the breast can- cer rate keeps climbing—incidence increased by more than 40% between 1973 and 1998, according to the Breast Cancer Fund—and The International Agency for Research on Cancer has classified shift work as a probable human carcinogen. A study in the December 2008 issue of Sleep found that use of light exposure therapy, sunglasses, and a strict sleep schedule may help night-shift workers achieve a better-balanced circadian rhythm. ferson University in Philadelphia. “That association does not prove that artificial light causes the prob- lem. On the other hand, controlled laboratory studies do show that exposure to light during the night can disrupt circadian and neuro endocrine physi- ology, thereby accelerating tumor growth.” The 24-hour day/night cycle, known as the circadian clock, affects physiologic pro- cesses in almost all organisms. These pro- cesses include brain wave patterns, hormone production, cell regulation, and other bio- logic activities. Disruption of the circadian clock is linked to several medical disorders in humans, including depression, insomnia, cardiovascular disease, and cancer, says Paolo Sassone-Corsi, chairman of the Pharmacology Department at the University of Cali fornia, Irvine, who has done extensive research on the circadian clock. “Studies show that the circadian cycle controls from ten to fifteen percent of our genes,” he explains. “So the disruption of the circadian cycle can cause a lot of health problems.” On 14–15 September 2006 the National Institute of Environmental Health Sciences (NIEHS) sponsored a meeting that focused on how best to conduct research on possible connections between artificial lighting and human health. A report of that meeting in the September 2007 issue of EHP stated, “One of the defining characteristics of life in the modern world is the altered patterns of light and dark in the built environment made possible by use of electric power.” The meeting report authors noted it may not be entirely coincidental that dramatic increases in the risk of breast and prostate cancers, obesity, and early-onset diabetes have mir- rored the dramatic changes in the amount and pattern of artificial light generated dur- ing the night and day in modern societies over recent decades. “The science underly- ing these hypotheses has a solid base,” they wrote, “and is currently moving forward rapidly.” The connection between artificial light and sleep disorders is a fairly intuitive one. Difficulties with adjusting the circadian clock can lead to a number of sleep disorders, including shift-work sleep disorder, which affects people who rotate shifts or work at night, and delayed sleep–phase syndrome, in which people tend to fall asleep very late at night and have difficulty waking up in time for work, school, or social engagements. The sleep pattern that was the norm before the invention of electric lights is no longer the norm in countries where artificial light extends the day. In the 2005 book At Day’s Close: Night in Times Past, historian Roger Ekirch of Virginia Polytechnic Insti- tute described how before the Industrial Age people slept in two 4-hour shifts (“first sleep” and “second sleep”) separated by a late-night period of quiet wakefulness. Thomas A. Wehr, a psychiatrist at the National Institute of Mental Health, has studied whether humans would revert back to the two-shift sleep pattern if they were not exposed to the longer photoperiod afforded by artifi- cial lighting. In the June 1992 Journal of Sleep Research, Wehr reported his find- ings on eight healthy men, whose light/dark schedule was shifted from their customary 16 hours of light and 8 hours of dark to a schedule in which they were exposed to natu- ral and electric light for 10 hours, then darkness for 14 hours to simulate natural durations of day and night in winter. The subjects did indeed revert to the two-shift pattern, sleeping in two sessions of about 4 hours each sepa- rated by 1–3 hours of quiet wakefulness. Turtle hatchlings instinctively orient away from the dark silhouette of the night- time shore. Here hatchlings have been temporarily distracted by a bright lamp. Hatchlings and mother turtles distracted by shorefront lights can wander onto nearby roadways. been shown to affect both flora and fauna. For instance, prolonged exposure to artificial light prevents many trees from adjusting to sea sonal variations, according to Winslow Briggs’s chapter on plant responses in the 2006 book Ecological Consequences of Artificial Night Lighting. This, in turn, has implications for the wildlife that depend on trees for their natu- ral habitat. Research on insects, turtles, birds, fish, reptiles, and other wild- life species shows that light pollution can alter behaviors, foraging areas, and breeding cycles, and not just in urban centers but in rural areas as well. Sea turtles provide one dramatic example of how artificial light on beaches can disrupt behavior. Many species of sea turtles lay their eggs on beaches, with females returning for decades to the beaches where they were born to nest. When these beaches are brightly lit at night, females may be discouraged from nesting in them; they can also be disoriented by lights and wander onto nearby roadways, where they risk being struck by vehicles. Moreover, sea turtle hatchlings normally navigate toward the sea by orienting away from the elevated, dark silhouette of the landward horizon, according to a study pub- lished by Michael Salmon of Florida Atlantic University and colleagues in volume 122, number 1–2 (1992) of Behaviour. When there are artificial bright lights on the beach, newly hatched turtles become dis oriented and navigate toward the artificial light source, never finding the sea. Jean Higgins, an environmental special- ist with the Florida Wildlife Conservation Commission Imperiled Species Management Section, says disorientation also contributes to dehydration and exhaustion in hatchlings. “It’s hard to say if the ones that have made it into the water aren’t more susceptible to pre- dation at this later point,” she says. Bright electric lights can also disrupt the behavior of birds. About 200 species of birds fly their migration patterns at night over North America, and especially during inclement weather with low cloud cover, they routinely are confused during passage by brightly lit buildings, communication towers, and other structures. “Light attracts birds and disorients them,” explains Michael Mesure, executive director of the Toronto-based Fatal Light Awareness Program (FLAP), which Glare, overillumination, and sky glow (which makes the sky over a city look orange, yellow, or pink) are all forms of light pollution. These photos were taken in Goodwood, Ontario, a small town about 45 minutes northeast of Toronto during and the night after the regionwide 14 August 2003 blackout. The lights inside the house in the blackout picture were created by candles and flashlights. cd/m2 candela/m2 0.2919 foot-Lamberts fl FORCE and PRESSURE or STRESS N newtons 0.225 poundforce lbf kPa kilopascals 0.145 poundforce per square inch lbf/in2 *SI is the symbol for th e International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380. (Revised March 2003) MOUNTAIN VISTA DR BUSCH DR LADY MOON DR W PLUM ST W VINE DR BINGHAM HILL RD ELM ST W LAKE ST ZEPHYR RD TILDEN ST STRAUSS CABIN RD S SUMMIT VIEW DR E PITKIN ST N TIMBERLINE RD N COUNTY ROAD 5 W STUART ST S CENTENNIAL DR ZIEGLER RD CONIFER ST DUNBAR AVE FRONTAGE RD E WILLOX LN WOOD ST GREGORY RD TURMAN DR CUSTER DR E ELIZABETH ST CENTRE AVE GIDDINGS RD W WILLOX LN CORBETT DR MCMURRY AVE E COUNTY ROAD 38 COLUMBIA RD ROCK CREEK DR WHEATON DR HAMPSHIRE RD PINE WABASH ST KECHTER RD MINUTEMAN DR BUCKSKIN TRL WELCH ST CHASE DR FOSSIL CREEK PKWY TULANE DR CLYDE ST CINQUEFOIL LN RIM ROCK TRL CARIBOU DR OAKRIDGE DR LINDEN LAKE RD LINDEN ST SUNSTONE DR W DOUGLAS RD ANTELOPE RD SHORE RD SPRINGFIELD DR MICHAUD LN YORKSHIRE ST S COUNTY ROAD 3F MCCLELLAND DR E COUNTY ROAD 36 MIDPOINT DR 12TH ST SPRING MESA RD BRITTANY DR CITY PARK AVE MAPLE ST AVONDALE RD WESTRIDGE DR S COUNTY ROAD 9 N COUNTY ROAD 23 S US HIGHWAY 287 NW FRONTAGE RD N COUNTY ROAD 9 SPRING CANYON RANCH RD SHORELINE DR E HORSETOOTH RD MEADOWLARK AVE W PITKIN ST E COUNTY ROAD 50 E COUNTY ROAD 48 EAST DR S COUNTY ROAD 11 FOSSIL CREEK DR 9TH ST RAMPART RD DUFF DR LYNDA LN N COUNTY ROAD 17 KYLE AVE MARIAH LN BOLTZ DR WESTGATE DR STANFORD RD JOHN F KENNEDY PKWY LAWTON LN ROYAL DR PARKER ST S HOWES ST SYKES DR SKYLINE DR LA EDA LN E LOCUST ST MATHEWS ST RANGER DR KIRKWOOD DR ELGIN CT N SUNSET ST E OLIVE ST BRUNS DR CENTENNIAL RD E COUNTY ROAD 54 ANN ST S GRANT AVE WILD VIEW DR COUNTY ROAD 42C REGENCY DR HULL ST E SKYWAY DR LINDENWOOD DR E OAK ST HUGHES WAY E COUNTY ROAD 52 ELM EAGLE DR IRISH DR COLONY DR HINSDALE DR E LAKE ST PLATTE DR BAR HARBOR DR N LINK LN MCLAUGHLIN LN CREST RD E COUNTY ROAD 32E BLUE SPRUCE DR STRACHAN DR TYLER ST HOGAN DR N GRANT AVE KEENLAND DR REMINGTON ST ARROWHEAD RD WATERGLEN DR RED FOX RD AIRPARK DR HILLSIDE DR E SATURN DR OLD MILL RD GOODELL LN BREAKWATER DR LIMON DR CANAL DR S LINK LN HEMLOCK ST WAKONDA DR POST RD MARS DR THOREAU DR PARKLAKE DR HAVEL AVE 1ST ST RAWHIDE DR OHIO IOWA ABBOTSFORD ST SHALLOW POND DR GARFIELD ST VALLEY FORGE AVE DENVER DR SNOW MESA DR MANHATTAN AVE ZURICH DR VICOT WAY PEYTON DR N HOLLYWOOD ST LANDINGS DR SPAULDING LN OFF RAMP S COUNTY ROAD 7 SHEELY DR EASTWOOD DR IDLEDALE DR RULE DR JOSEPH ALLEN DR WEITZEL ST JAY DR HEARTHFIRE DR FORECASTLE DR HEADWATER DR APPLE DR SHARP POINT DR THOREAU RD MOORE LN PECAN ST CAMINO REAL TICONDEROGA DR BRYCE DR WINDOM ST KITCHELL WAY ALLOTT AVE CAJETAN ST N IMPALA DR ACADEMY CT FALL HARVEST WAY LYNN DR YEARLING DR EDITH DR VICTORIA DR RED OAK CT LE FEVER DR S BRYAN AVE N MASON ST CASA LN WHITE WILLOW DR HOFFMAN MILL RD FLEET DR MACKINAC ST E COUNTY ROAD 40 TAMARISK DR SCENIC DR SHIRE CT CARMICHAEL ST WAPITI RD CAMPFIRE DR ON RAMP INTERNATIONAL BLVD LUKE ST N COUNTY ROAD 23E FAIRWAY LN WHALERS WAY MCHUGH ST STREAMSIDE DR WOOD LN SILVERGATE RD ROSS DR TRUXTUN DR DIXON COVE DR BUTTE PASS DR HILLDALE DR CLOVERLEAF WAY SNOWY PLAIN RD MIDWAY DR BISON RD HEPPLEWHITE CT MERGANSER DR LOWELL LN WILMINGTON DR HOLYOKE CT THARP DR DAKOTA ST BOXELDER DR CINDY LN SMOKEY ST PARK PLACE DR BRENTON DR FIELDSTONE DR BIPLANE ST WINFIELD DR QUEST DR DELANY DR ENFIELD ST LAREDO LN TERRY RIDGE RD BUCHANAN ST WAYNE ST CHESAPEAKE DR ROOKERY RD PARKSIDE DR CARRIAGE RD KITTERY CT PAWNEE DR ORILLA DEL LAGO SHILO DR COVENTRY CT EASTBROOK DR PAVILION LN TRESTLE RD BUCKEYE ST SILVER MIST LN WEBSTER AVE CATALINA DR ESSEX DR DAVIDSON DR BOXELDER ST PRADO DR NEIL DR ALCOTT ST S SHERWOOD ST KRISRON RD ORBIT WAY MEADOW LN MEDLAR PL SUNRISE DR BIANCO DR NELSON LN 11TH ST ERIC ST KINGSTON DR BAYSIDE DR BRUMBY LN HARVEST PARK LN RELIANT ST WINAMAC DR DONELLA CT LOWE ST SUNRISE LN CATALPA DR SAN LUIS ST QUEENS CT CHARLIE LN MARBLE DR SHANNON DR CABOT CT BOONE ST LYMEN ST WINGFOOT DR VINSON ST MERCY DR TEAL DR DAYTON DR BATELEUR LN DEER TRAIL CT CHERRY ST S MASON ST FRONTAGE RD OFF RAMP MOORE LN MAX GUIDEWAY S LEMAY AVE OFF RAMP E COUNTY ROAD 30 W LAKE ST W LAKE ST STOVER ST SW FRONTAGE RD W VINE DR S COUNTY ROAD 5 OFF RAMP INTERSTATE 25 E DRAKE RD SE FRONTAGE RD MAIN ST S TIMBERLINE RD TURNBERRY RD MAPLE ST ELM ST OFF RAMP ON RAMP Legend Downtown Zone City of Fort Collins City Limits Growth Management Area !( Billboard Sign Location ¯ 0 0.5 1 1.5 2 2.5 3 Miles ATTACHMENT 1