Statistical Analysis of the Safety Impacts of Digital On-Premise Signs

1. Statistical Analysis of the Safety Impacts of Digital On-Premise Signs H. Gene Hawkins, Jr., Ph.D., P.E. Texas A&M University Presented at the National Signage Research and Education Conference Cincinnati, Ohio; October 11, 2012

2. http://www.google.com/imgres?hl=en&client=firefox-a&hs=z6f&sa=X&rls=org.mozilla:en-US:official&biw=1567&bih=947&tbm=isch&prmd=imvns&tbnid=re7a7BmzF9R4ZM:&imgrefurl=http://www.thesavvyboomer.com/the_savvy_boomer/2008/04/using-your-cell.html&docid=qg392gIw7AG45M&imgurl=http://www.thesavvyboomer.com/photos/uncategorized/2008/04/07/cell_phone_driver.png&w=450&h=287&ei=SSNWUPCVD4G9qQHIuIDQCQ&zoom=1&iact=hc&vpx=735&vpy=346&dur=11&hovh=179&hovw=281&tx=120&ty=88&sig=108482171543026786120&page=1&tbnh=134&tbnw=173&start=0&ndsp=35&ved=1t:429,r:17,s:0,i:128http://www.google.com/imgres?hl=en&client=firefox-a&hs=z6f&sa=X&rls=org.mozilla:en-US:official&biw=1567&bih=947&tbm=isch&prmd=imvns&tbnid=re7a7BmzF9R4ZM:&imgrefurl=http://www.thesavvyboomer.com/the_savvy_boomer/2008/04/using-your-cell.html&docid=qg392gIw7AG45M&imgurl=http://www.thesavvyboomer.com/photos/uncategorized/2008/04/07/cell_phone_driver.png&w=450&h=287&ei=SSNWUPCVD4G9qQHIuIDQCQ&zoom=1&iact=hc&vpx=735&vpy=346&dur=11&hovh=179&hovw=281&tx=120&ty=88&sig=108482171543026786120&page=1&tbnh=134&tbnw=173&start=0&ndsp=35&ved=1t:429,r:17,s:0,i:128 The Problem • Information, information, + more information • Traffic signs, in-vehicle displays, GPS navigation, billboards, business signs, etc. • As more information is presented, there is more competition for attention

3. Businesses • Business owners want customers: • Loyal/repeat customers (familiar/old) • Know the way, just need reminder • New customers (unfamiliar/new) • Need to find the way • Both customer groups rely upon signs for navigating to the business • Business owners also rely upon signs for advertising and marketing

4. Types of Signs • Traffic signs • In the roadway right-of-way • Regulate, warn, and guide traffic • On-premise signs • On business building or property • Get traffic to business • Off-premise signs • Away from business • Generate interest in business

5. www.textually.org Competition for Attention • Drivers deal with: • Vehicle control • Steering + brakes • Cockpit controls • Environment • Music • GPS • Navigation • Short-term • Long-term • Other challenges • Increased traffic, higher speeds, more controls • Distractions • Phones (talking) • Phones (texting) • Phones (email) • Reading (books) • Listening (books) • Eating • Drinking • Make-up • Passengers • Other vehicles

6. Recent Example • From Bryan-College Station Eagle

7. How to Attract Attention • Make it: • Bigger • Brighter • Add motion/movement • Increase contrast • Applies to all visual stimuli • Traffic signs, business signs, people, objects, etc.

8. Signage Advances • Recent technologies allow: • Electronic signs or panels • Custom messages • Animation • Video • LEDs and other advancements provide more features at lower costs • Potential concern that electronic displays increase driver distraction 2003 2012

9. The Challenge • Many issues combine to create a challenge: • Advanced sign technologies are relatively new • Have spread rapidly • While traffic sign research is sponsored by public agencies, there is not comparable research program for business signs • Concerns over traffic safety • Base issue: Do electronic message signs create a distraction problem that increases crash risk? • Result: Has led some local agencies to establish sign codes that are based on opinion more than scientific fact

10. Our Research Study • Goal: • Determine if the installation of digital on-premise signs has an impact on traffic safety in the area around such signs • Highlights: • Scientific procedure • Robust crash dataset • Large sample size of sign locations • Advanced statistical analysis methods

11. Digital Sign Definition • Target: on-premise digital sign • Located on business property • Sign uses electrical display • Provides changeable message

12. Related Research • Limited research conducted on business signs • On-premise signs: • Mace (2001): synthesis of literature • Hypothesized that distraction potential of signs could compromise safety • Hypothesized benefits as navigational aid • No data collection to support or refute claims • Wachtel (2009): synthesis suggested on-premise signs affect safety more than off-premise signs • Because locations and elevations of on-premise signs might be closer to the road users • Angles of on-premise signs may be out of vision core and require extreme head movements • Conclusions of both are based on educated judgment rather than scientific analysis

13. Off-Premise Signs • FHWA study by Molino et al. (2009) • Meta analysis of 32 previous studies • Focus on billboards • Most, but not all, of previous research shows negative safety impact • Although safety issues not resolved, there is more analysis of off-premise signs than on-premise

14. Knowledge Gap • Little knowledge about safety impacts of on-premise signs • What is known is not based on detailed scientific analysis • Inconsistent findings • Weaknesses • Inadequate sample sizes • Inappropriate statistical analysis methods • Short time frames for analysis (before & after) • At present, cannot define safety impacts of digital signs

15. Research Approach • Highlights: • Collect sign data • Specific to on-premise digital signs • Installation date and location are critical • Collect crash data at locations where signs are located • Crash information: date, location, type, etc • Need several years of data • Develop statistical analysis procedure • Perform safety analysis

16. Sign Data Acquisition • Required significant effort • Initial attempts not successful • Asking for too much • Refined request based on crash data dates and states • Installed in 2006 or 2007 • Insures adequate before and after periods • Located in California, Illinois, Maine, Minnesota, North Carolina, Ohio, or Washington • These are states with crash data

17. Sign Data Sets • Sign datasets acquired from two companies • #1: 2,953 sites with 27 variables • Variables: date, address, cross-street, road/traffic information, etc. • Road/traffic information not used • #2: 63 sites with 10 variables • Sign locations had to be confirmed through Google Earth/Map to be usable

18. Sign Data Processing • Raw sign data required significant processing to be useable • Sites eliminated due to: • Installed before 2006 or after 2007 • These limits provided sufficient date in both before and after analysis periods • Year of installation not included in analysis • Location could not be confirmed in Google Earth or Google Maps (Street View) • Analysis area defined as within 0.1 mile of target sign

19. Crash Data Background • Comprehensive crash data is limited and hard to obtain • Belongs to agencies • Protected information for liability reasons • Expensive to maintain • National databases • FARS: Fatal Accident Reporting System • NHTSA owned – no info on crash location • HSIS: Highway Safety Information System • FHWA owned – significant related information

20. HSIS Crash Data • HSIS provided the best means of performing a detailed national analysis of crash impacts due to digital signs • Contains crash, roadway, and vehicle information • Operated/maintained by FHWA • Widely used for safety research • Multi-state data • California, Illinois, Maine, Minnesota, North Carolina, Ohio, and Washington • We chose to focus on CA, NC, OH, and WA due to limited sign locations in IL, ME, and MN

21. HSIS File Types • Crash files: • Location, date, time, light, weather conditions, severity, number of related vehicles, collision type • Driver and vehicle files: • Driver gender, age, contributing factor (possible casual factor), vehicle type • Road and traffic files : • Traffic data – Average Daily Traffic (ADT), speed limit • Road data – number of lanes, lane and median width, shoulder width and type, rural or urban designation, and functional classifications. • All files are linked to provide for robust analysis • Example: crash frequency and traffic volume can be combined to present a crash rate at a location

22. Data Analysis Steps • General steps • Confirm location of signs • Convert sign location to crash location • Evaluate site qualification factors • Conduct statistical analysis • Each is explained in upcoming slides

23. Confirm Sign Location • Use Google Map and Google Earth • Verify location of each target sign • Used street address provided in sign data set • Confirm that it is a digital sign • Confirm that is it an on-premise sign • Confirm still in place (as of the date of the Google Street View image) • Measure milepost from county boundary • Used to link location to crash data

24. Confirm Sign Location • Labor intensive and time consuming activity • Used student workers to process data

25. Combine Sign and Crash Data • Sign and crash data use different location systems • Sign data based on street address • Crash data based on route and milepost • Convert sign location to route and milepost format and combine with crash location data set

26. Site Qualification Factors • We only retained the sign sites satisfying the following conditions: • Located in CA, NC, OH, or WA • Installed between 2006-2007 • Located on the major roads • With at least one crash record in the before or after period

27. Sign Data Yield Rates • After processing sign and crash data, the number of sites usable for analysis was a fraction of initial number • Initial sign data sets: 2,953 + 63 = 3,016 • Overall yield rate = 126/3016 = 4.2%

28. Statistical Analysis Options • Options for analyzing safety impacts: • Before-after study • Crashes in the period before improvement compared to crashes in the period after the improvement • Provides more direct evaluation • Cross-sectional study • Crashes on a facilities with the improvement compared to crashes on similar facilities without the improvement • Different facilities rarely identical in all features • We chose to use before-after study

29. Types of Before-After • Naïve before-after study • Simple comparison • Results may be influenced by factors not accounted for • Not a preferred analysis method • Before-after study with control group • Control group helps to account for external influences that could affect results • Requires additional data for control locations • Difficult to identify appropriate control locations with same characteristics • Before-after study using the empirical Bayes (EB) method

30. Empirical Bayesian Method • Recommended as preferred method in Highway Safety Manual • Combines short-term observed crash numbers with crash prediction model data to obtain a more accurate estimation of the long-term crash mean • Example: a new driver has no crashes during first year of driving • Typical new driver has 0.08 crashes per year • Not reasonable to expect 0 or 0.08 crashes in second year • EB would provide an estimate that is a mixture of these two values by considering safety of a specific segment and safety of a typically similar road

31. EB Analysis Formula • General form: • Where:

32. Preliminary Study Results • Interpretation factors • All crashes • For entire sample • For each state • Single and multiple vehicle crashes • For entire sample • For each state • These results are preliminary • Research in final stages of completion

33. Interpreting Results • Using the EB method, there is no statistically significant change in crashes if the confidence interval for EB contains 1

34. Preliminary Results for All Crash Types  No. of Sites Sample Size

35. Preliminary Results for Single Vehicle Crashes  No. of Sites Sample Size

36. Preliminary Results for Multiple Vehicle Crashes  No. of Sites Sample Size

37. Preliminary Conclusions • For the entire sample: • We did not find a statistically significant impact between the installation of digital signs and an increase in crashes within 0.1 mile of the signs’ locations • For multiple-vehicle crashes • We found the same result • For single-vehicle crashes • We found the same result except for California, which is likely due to the smaller sample size and the resulting decrease in statistical certainty (increased probability of error)