1. Evaluation of Law Enforcement Presence on Changing Drivers’ Behaviors – Red Light Running International Traffic Records Forum July 2003

2. Funding & Participants Funding – Joint Transportation Research Program Purdue University Co-PIs - Andrzej Tarko – Civil Engineering, Purdue University - Robert Zahnke – Center for the Advancement of Transportation Safety (CATS), Purdue University - Maria Drake - Clifford Stover - Jose Thomaz - John Ragan - Carolyn Bridge

3. Project Objectives Assessment of presence/seriousness of RLR Effectiveness of a media campaign Development of enforcement model

4. Approaches Civil Engineering  Random Telephone Survey  Longitudinal Survey of Single intersection  24/7 video monitoring CATS  Literature Review  Cross-Section Observational monitoring  Crash/citation data  Media/law enforcement

5. Center for the Advancement of Transportation Safety (CATS) Selection of intersections Observational survey protocols Survey schedules Results Media/enforcement efforts Crash/citation correlation Conclusions Recommendations for the future

6. Selection of Intersections Compiling the inventory  Categorizing sites into “buckets”  Criteria/attributes Speed limit Traffic volume Roadway configuration & intersection design Number of lanes Turn lanes

7. Observational Survey Protocols Data captured  Date/observation time  5-minute pre/post count (traffic volume)  Direction of vehicle  Vehicle type  Gender/race/approximate age

8. Observation Form

9. Observational Survey Schedules All days of the week Daylight hours  Early morning rush hour  Mid Morning  Lunch  Mid Afternoon  Evening Rush  Evening

10. Number of RLRs by Type of Vehicle Maneuver

11. Number of RLR Violations by Traffic Volume

12. Number of RLRs per 45-Minute Observation Period by Observation Starting Time

13. Number of RLRs by Driver Gender and Age Group Driver Gender YoungAverageOlder Female45.2%33.2%31.5% Male54.8%66.8%68.5%

14. Number of RLRs by Driver Race RaceCountsPercent Caucasian83887.6% Other474.9% African American313.2% Unknown212.2% Data Not Collected202.1% Total957100.0%

15. Collapsing the Data Vehicle TypeCountsPercent Passenger Cars52655.0% Pickup Trucks17918.7% Mini or Large Vans10010.4% Sport Utility Vehicles10010.4% Motorcycles90.9% Other Vehicle Types434.5% Total957100.0%

16. Vehicle Maneuver by Vehicle Type Vehicle Type Vehicle Maneuver LeftRightStraight Passenger Cars34.7%11.3%54.0% Pickup Trucks33.1%16.1%50.8% Mini & Large Vans37.1%12.9%50.0% Others25.0%15.9%59.1% Sport Utility Vehicles 29.2%16.7%54.2% Total for All Vehicles 33.4%13.2%53.4%

17. Distribution of Intersection Crashes by Time of Day in Lafayette and West Lafayette, 1995-1999

18. Distribution of RLR Crashes by Time of Day in Lafayette & West Lafayette, 1995-1999

19. Distribution of RLR

20. Signalized Intersection Crashes in Lafayette and West Lafayette in 1999

21. Distribution of Intersection Crashes by Day of Week in Lafayette and West Lafayette, 1995-1999

22. Percentage of Signalized Intersection Crashes Due to RLR in Lafayette and West Lafayette in 1999

23. RLR Observations

24. Results – 3 rd Round Targeted Highest Rates of Incidence – Location and Time of 2 Sites  Intersection of a state highway and US highway  5-point intersection  Lunch time

25. Media/Enforcement efforts Media  Print – local newspaper  Television/radio Enforcement  Saturation patrols  Parked vehicles

26. Media/Enforcement Results Media – 3 types  By itself had no effect on reducing RLR Enforcement  Only when visibly present

27. Overall Conclusions RLR occurs during all hours, on all days of week, and on all types of roads Range: 4 – 18 violations per hour Peaked Lunch time Occurrences increased as work week progressed No common, typical violator or scenario— varied vehicle, driver, or intersection types More likely to encounter RLR at higher traffic density intersections

28. Other Observations Over 50% of RLR violators observed were proceeding straight through intersection when red light was run Ratio of passenger car violators and pickup truck violators paralleled Indiana vehicle registrations (no over-representation noted) Young Male to Young Female ratio 1:1 Average Male to Average Female ratio 2:1 Older Male to Older Female ratio 2:1 Race of violators matched IN Census Bureau counts (no over-representation noted)

29. Other Observations Signage had an immediate impact on reducing RLR at video-monitored intersection

30. For further information Bob Zahnke  Purdue University  rzahnke@ecn.purdue.edu rzahnke@ecn.purdue.edu  765-496-3716  CATS web site – www.ecn.purdue/cats JTRP link  http://rebar.ecn.purdue.edu/JTRP