Safety impacts of red light running photo enforcement at urban signalized intersections

Red light running at signalized intersections is a major safety concern in the United States. Statistics show that approximately 45 percent of crashes at intersections caused by red light running result in severe injuries and fatalities, while only approximately 30 percent of all other types of intersection crashes cause injuries or fatalities. Over the past decade, many US cities and counties have deployed red light running photo enforcement systems for signalized intersections within their jurisdictions to potentially reduce red light running related crashes. This study proposes an empirical Bayesian (EB) before-after analysis method that computes a weighed sum of crashes observed in the field and crashes predicted by safety performance functions (SPFs) to mitigate regression-to-mean biases for analyzing crash reduction effects of red light running enforcement. The analysis explicitly considers red light running related crash types, including head-on, rear-end, angle, turning, sideswipe in the same direction, and sideswipe in the opposite direction; and crash severity levels classified as fatal, injury, and property damage only (PDO). A computational study is conducted to examine the effectiveness of the Chicago program with red light running photo enforcement systems deployed for nearly two hundred signalized intersections. It is revealed that the use of red light running photo enforcement on the whole is positive, as demonstrated by reductions in all types of fatal crashes by 4-48 percent, and injury-related angle crashes by 1 percent. However, it slightly raises PDO-related angle crashes and moderately increases injury and PDO related rear-end crashes. The safety effectiveness of red light running photo enforcement is sensitive to intersection location. Language: en

[1]  Yiik Diew Wong,et al.  THE IMPACT OF RED-LIGHT SURVEILLANCE CAMERAS ON ROAD SAFETY IN SINGAPORE , 1997 .

[2]  Bhagwant Persaud,et al.  Empirical Bayes before-after safety studies: lessons learned from two decades of experience and future directions. , 2007, Accident; analysis and prevention.

[3]  John McFadden,et al.  Automated Enforcement of Red Light Running Technology and Programs: A Review , 2000 .

[4]  J. S. Long,et al.  Regression Models for Categorical and Limited Dependent Variables , 1997 .

[5]  Zongzhi Li,et al.  New Methodology for Intersection Signal Timing Optimization to Simultaneously Minimize Vehicle and Pedestrian Delays , 2014 .

[6]  Allan F. Williams,et al.  Evaluation of Red Light Camera Enforcement in Fairfax, Va., USA , 1999 .

[7]  Nataliya V Malyshkina,et al.  Zero-state Markov switching count-data models: an empirical assessment. , 2008, Accident; analysis and prevention.

[8]  Zhuo Li,et al.  Modeling motor vehicle crashes for street racers using zero-inflated models. , 2008, Accident; analysis and prevention.

[9]  A F Williams,et al.  Evaluation of red light camera enforcement in Oxnard, California. , 1999, Accident; analysis and prevention.

[10]  Richard A Retting,et al.  Reductions in injury crashes associated with red light camera enforcement in oxnard, california. , 2002, American journal of public health.

[11]  Hugh W. Mcgee,et al.  IMPACT OF RED LIGHT CAMERA ENFORCEMENT ON CRASH EXPERIENCE , 2003 .

[12]  Bhagwant Persaud,et al.  Comparison of empirical Bayes and full Bayes approaches for before-after road safety evaluations. , 2010, Accident; analysis and prevention.

[13]  John McFadden,et al.  SYNTHESIS AND EVALUATION OF RED LIGHT RUNNING AUTOMATED ENFORCEMENT PROGRAMS IN THE UNITED STATES , 1999 .

[14]  D. Lindley,et al.  Bayes Estimates for the Linear Model , 1972 .

[15]  Ezra Hauer,et al.  Estimating Safety by the Empirical Bayes Method: A Tutorial , 2002 .

[16]  Pravin K. Trivedi,et al.  Regression Analysis of Count Data , 1998 .

[17]  Kofi Obeng,et al.  A detailed investigation of crash risk reduction resulting from red light cameras in small urban areas , 2004 .

[18]  Jake Kononov Identifying Locations with Potential for Accident Reductions: Use of Direct Diagnostics and Pattern Recognition Methodologies , 2002 .

[19]  Susan A Ferguson,et al.  Effects of Red Light Cameras on Violations and Crashes: A Review of the International Literature , 2003, Traffic injury prevention.

[20]  Xuesong Wang,et al.  Modeling signalized intersection safety with corridor-level spatial correlations. , 2010, Accident; analysis and prevention.

[21]  S. Emerson,et al.  AASHTO (American Association of State Highway and Transportation Officials). 2001. A Policy on Geometric Design of Highways and Streets. Fourth Edition. Washington, D.C. , 2007 .

[22]  John N. Ivan New Approach for Including Traffic Volumes in Crash Rate Analysis and Forecasting , 2004 .

[23]  Sunil Madanu,et al.  Life-Cycle Cost Analysis of Highway intersection Safety Hardware Improvements , 2010 .

[24]  Ezra Hauer,et al.  OBSERVATIONAL BEFORE-AFTER STUDIES IN ROAD SAFETY -- ESTIMATING THE EFFECT OF HIGHWAY AND TRAFFIC ENGINEERING MEASURES ON ROAD SAFETY , 1997 .

[25]  John N. Ivan,et al.  Hierarchical Bayesian Estimation of Safety Performance Functions for Two-Lane Highways Using Markov Chain Monte Carlo Modeling , 2005 .

[26]  A. Williams,et al.  Prevalence and characteristics of red light running crashes in the United States. , 1999, Accident; analysis and prevention.

[27]  Wassim G. Najm,et al.  ANALYSIS OF FATAL CRASHES DUE TO SIGNAL AND STOP SIGN VIOLATIONS , 2004 .

[28]  Brian S Bochner,et al.  Effectiveness of Red-Light Cameras , 2010 .

[29]  Bryan K Allery,et al.  Level of Service of Safety: Conceptual Blueprint and Analytical Framework , 2003 .

[30]  Dominique Lord,et al.  Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory. , 2005, Accident; analysis and prevention.

[31]  Shauna L. Hallmark,et al.  The Use of Statistical Evaluations to Investigate the Effectiveness of Iowa’s Automated Red Light Running Programs , 2008 .

[32]  Bruce N. Janson,et al.  Diagnostic Methodology for the Detection of Safety Problems at Intersections , 2002 .

[33]  K. M. Lum,et al.  A before-and-after study on green signal countdown device installation , 2006 .

[34]  Brian Malone,et al.  Evaluation of the Red Light Camera Enforcement Pilot Project , 2005 .

[35]  Dominique Lord,et al.  Further notes on the application of zero-inflated models in highway safety. , 2007, Accident; analysis and prevention.

[36]  Hoong Chor Chin,et al.  Applying the random effect negative binomial model to examine traffic accident occurrence at signalized intersections. , 2003, Accident; analysis and prevention.

[37]  Jack Lucero Fleck,et al.  Can We Make Red-Light Runners Stop?: Red-Light Photo Enforcement in San Francisco, California , 1999 .