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Evaluation of Law Enforcement Presence on Changing Drivers’ Behaviors – Red Light Running

Evaluation of Law Enforcement Presence on Changing Drivers’ Behaviors – Red Light Running. International Traffic Records Forum July 2003. Funding & Participants. Funding – Joint Transportation Research Program Purdue University Co-PIs - Andrzej Tarko – Civil Engineering,

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Evaluation of Law Enforcement Presence on Changing Drivers’ Behaviors – Red Light Running

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  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. 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 Approaches

  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

  14. Number of RLRs by Driver Race

  15. Collapsing the Data

  16. Vehicle Maneuver by Vehicle Type

  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 – 3rd 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. Signage had an immediate impact on reducing RLR at video-monitored intersection Other Observations

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

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