Lesson

Include driver age, time of day, and intersection characteristics in the design of red light violation algorithms and warning systems, and their field operational tests.

Knowledge gained from an analysis of four years of red light violation data gathered from 11 signalized intersections in Sacramento, California


March 2006
Sacramento,California,United States


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Lesson Learned

An analysis of four years of data from red light photo enforcement cameras at 11 signalized intersections in the City of Sacramento identified factors that are relevant to the design of violation warning algorithms, driver interfaces and field operational tests of such devices. The analysis revealed a correlation between driver age and the number of violations but also with the speed with which the driver enters the intersection. Similarly, the time of day correlated with the speed of intersection crossing and the amount of elapsed time after the red light onset before entering the intersection. The analysis suggests that researchers should consider the following in further work related to preventing red light violations via cooperative systems.
  • Examine driver age in red light violations and incorporate it in the design and testing of algorithms, driver warning interfaces and field operational tests of a CSVWS. The analysis showed that drivers younger than 30 years of age are more likely to violate a red light and tend to enter the intersection at speeds higher than the posted speed limit than most drivers. Younger drivers have varying behavior patterns that may influence their responsiveness to violation warnings.
  • Consider excluding gender as a variable in a field operational test. This analysis showed no relationship between red light violation behavior and driver gender. Eliminating gender as a variable may streamline the design and reduce the resources required to conduct an operational test.
  • Include sufficient number of research participants who are likely to drive at off-peak hours. The red light violations from 8 p.m. to 5 a.m. tended to be more aggressive in that the drivers were more likely to enter the intersection at speeds higher than the posted speed limit, and enter the intersection after the onset of the red light by more than 2 (two) seconds.
  • Consider varying the warning algorithm and interface by time period. The nature of the violation varied by time of day, with red light violators during working hours less likely than violators during off-peak hours to speed through the intersection or enter the intersection later than 2 (two) seconds after the red light onset. This difference in violator behavior suggests that a less decisive (and therefore less potentially annoying) warning may be effective for potential violators during peak hours. Conversely, it may be necessary for off-peak warnings to be stronger and issued earlier to motivate off-peak drivers to stop in time at the red light.
  • Include signalized intersections that vary by traffic volume and duration of clearance intervals (yellow time and all-red phase) in a field operational test. The analysis found that traffic volume and clearance intervals correlated with the likelihood of violators driving over the speed limit through an intersection, and entering the intersection when the elapsed time since the red light onset was more than 2 seconds.
The analysis of four years of data from 11 signalized intersections reveals factors that correlate with varying characteristics of red light violation behavior. The analysis should be helpful to researchers involved in the design of field operational tests, warning algorithms and warning interfaces and can improve the likelihood of deploying a successful violation-warning device. Extrapolating findings from real-world experiences to the development of innovative interventions such as the CSVWS is an efficient and logical way to enhance the safety on surface transportation operations.


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Source

Analysis of Red Light Violation Data Collected from Intersections Equipped with Red Light Photo Enforcement Cameras

Author: Yang, C. Y. David and Wassim G. Najm

Published By: Prepared by Volpe for the U.S. DOT FHWA

Source Date: March 2006

Other Reference Number: Report No. DOT HS 810 580

URL: http://permanent.access.gpo.gov/lps97800/810580.pdf

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Kathryn Wochinger
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Lesson ID: 2010-00562