Evaluation of countermeasures for red light running by traffic simulator–based surrogate safety measures

ABSTRACT Objective: The conflicts among motorists entering a signalized intersection with the red light indication have become a national safety issue. Because of its sensitivity, efforts have been made to investigate the possible causes and effectiveness of countermeasures using comparison sites and/or before-and-after studies. Nevertheless, these approaches are ineffective when comparison sites cannot be found, or crash data sets are not readily available or not reliable for statistical analysis. Considering the random nature of red light running (RLR) crashes, an inventive approach regardless of data availability is necessary to evaluate the effectiveness of each countermeasure face to face. Method: The aims of this research are to (1) review erstwhile literature related to red light running and traffic safety models; (2) propose a practical methodology for evaluation of RLR countermeasures with a microscopic traffic simulation model and surrogate safety assessment model (SSAM); (3) apply the proposed methodology to actual signalized intersection in Virginia, with the most prevalent scenarios—increasing the yellow signal interval duration, installing an advance warning sign, and an RLR camera; and (4) analyze the relative effectiveness by RLR frequency and the number of conflicts (rear-end and crossing). Results: All scenarios show a reduction in RLR frequency (−7.8, −45.5, and −52.4%, respectively), but only increasing the yellow signal interval duration results in a reduced total number of conflicts (−11.3%; a surrogate safety measure of possible RLR-related crashes). An RLR camera makes the greatest reduction (−60.9%) in crossing conflicts (a surrogate safety measure of possible angle crashes), whereas increasing the yellow signal interval duration results in only a 12.8% reduction of rear-end conflicts (a surrogate safety measure of possible rear-end crash). Conclusions: Although increasing the yellow signal interval duration is advantageous because this reduces the total conflicts (a possibility of total RLR-related crashes), each countermeasure shows different effects by RLR-related conflict types that can be referred to when making a decision. Given that each intersection has different RLR crash issues, evaluated countermeasures are directly applicable to enhance the cost and time effectiveness, according to the situation of the target intersection. In addition, the proposed methodology is replicable at any site that has a dearth of crash data and/or comparison sites in order to test any other countermeasures (both engineering and enforcement countermeasures) for RLR crashes.

[1]  Xuedong Yan,et al.  Red-light Running and Limited Visibility Due to LTV's using the UCF Driving Simulator Contract BD548, RPWO #1 , 2005 .

[2]  S. Arabia UNIVERSITY OF KANSAS , 2008 .

[3]  ROBERT H. WORTMAN,et al.  Evaluation of Driver Behavior at Signalized Intersections , 2017 .

[4]  Maurizio Guida,et al.  Microsimulation Approach for Predicting Crashes at Unsignalized Intersections Using Traffic Conflicts , 2012 .

[5]  C. Hydén THE DEVELOPMENT OF A METHOD FOR TRAFFIC SAFETY EVALUATION: THE SWEDISH TRAFFIC CONFLICTS TECHNIQUE , 1987 .

[6]  Christopher M Cunningham,et al.  Evaluating the Use of Red Light Running Photographic Enforcement Using Collisions and Red Light Running Violations , 2004 .

[7]  윤태영,et al.  Transportation Research Board of the National Academies , 2015 .

[8]  Yanfeng Ouyang,et al.  Development and Application of Safety Performance Functions for Illinois , 2010 .

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

[10]  Martin R Parker,et al.  Traffic conflict techniques for safety and operations: engineers guide , 1989 .

[11]  William Young,et al.  Signal Treatments to Reduce the Likelihood of Heavy Vehicle Crashes at Intersections: Microsimulation Modeling Approach , 2010 .

[12]  Larry Head,et al.  Surrogate Safety Measures from Traffic Simulation Models , 2003 .

[13]  Hamid Behbahani,et al.  Calibration and validation of a new time-based surrogate safety measure using fuzzy inference system , 2016 .

[14]  Craig Lyon,et al.  Safety evaluation of red-light cameras , 2005 .

[15]  Shauna L. Hallmark,et al.  Toolbox of Countermeasures to Reduce Red Light Running , 2012 .

[16]  Kay Fitzpatrick,et al.  Driver Braking Performance in Stopping Sight Distance Situations , 2000 .

[17]  Arlinda Alimehaj Rrecaj,et al.  Calibration and Validation of the VISSIM Parameters - State of the Art , 2015 .

[18]  Tarek Sayed,et al.  Surrogate Safety Assessment Model and Validation: Final Report , 2008 .

[19]  G. M. Davis The Department of Transportation , 1970 .

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

[21]  Á. Seco,et al.  Safety analysis of turbo-roundabouts using the SSAM technique , 2013 .

[22]  Prathmesh Argade Quantifying the Safety Effects of Access Management Using VISSIM and SSAM: A Case Study , 2014 .

[23]  Christopher M. Monsere,et al.  Calibration of Highway Safety Manual Predictive Models for Oregon State Highways , 2011 .

[24]  Hideki Nakamura,et al.  Safety Evaluation for Intergreen Intervals at Signalized Intersections Based on Probabilistic Method , 2009 .

[25]  Bhagwant Persaud,et al.  Integrated traffic conflict model for estimating crash modification factors. , 2014, Accident; analysis and prevention.

[26]  Aleksandar Stevanovic,et al.  Assessment of Surrogate Safety Benefits of an Adaptive Traffic Control System , 2011 .

[27]  Wael K. M. Alhajyaseen,et al.  The Development of Conflict Index for the Safety Assessment of Intersections Considering Crash Probability and Severity , 2014, ANT/SEIT.

[28]  F Navin,et al.  Simulation of traffic conflicts at unsignalized intersections with TSC-Sim. , 1994, Accident; analysis and prevention.

[29]  Fred L. Mannering,et al.  The relationship among highway geometrics, traffic-related elements and motor-vehicle accident frequencies , 1998 .

[30]  Katja Vogel,et al.  A comparison of headway and time to collision as safety indicators. , 2003, Accident; analysis and prevention.

[31]  Simon Washington,et al.  The Impact of Red Light Cameras (Automated Enforcement) on Safety in Arizona , 2005 .

[32]  Nalini Ravishanker,et al.  Selecting exposure measures in crash rate prediction for two-lane highway segments. , 2004, Accident; analysis and prevention.

[33]  John Hourdos,et al.  Opportunities to Preventing Rear-End Vehicle Crashes: Findings from Analyzing Actual Crash Data , 2010 .

[34]  David A Noyce,et al.  Analysis of Driver Behavior in Dilemma Zones at Signalized Intersections , 2007 .

[35]  H W McGee,et al.  ENGINEERING COUNTERMEASURES TO REDUCE RED LIGHT RUNNING , 2002 .

[36]  P. Vedagiri,et al.  Proactive Evaluation of Traffic Safety at An Unsignalized Intersection Using Micro- Simulation , 2014 .

[37]  Robert Herman,et al.  The Problem of the Amber Signal Light in Traffic Flow , 1960 .

[38]  A. Horst A time-based analysis of road user behaviour in normal and critical encounters , 1990 .

[39]  Alexander Skabardonis,et al.  Traffic Analysis Toolbox Volume III: Guidelines for Applying Traffic Microsimulation Modeling Software , 2004 .

[40]  Eric J Fitzsimmons The effectiveness of Iowa's automated red light running enforcement programs , 2007 .

[41]  Jaehyun So,et al.  Development and Validation of a Vehicle Dynamics Integrated Traffic Simulation Environment Assessing Surrogate Safety , 2015, J. Comput. Civ. Eng..

[42]  Jeroen Hogema,et al.  TIME-TO-COLLISION AND COLLISION AVOIDANCE SYSTEMS , 1994 .

[43]  Nithin K. Agarwal,et al.  ESTIMATION OF PEDESTRIAN SAFETY AT INTERSECTIONS USING SIMULATION AND SURROGATE SAFETY MEASURES , 2011 .

[44]  Anne T McCartt,et al.  Effects of red light camera enforcement on fatal crashes in large U.S. cities. , 2011, Journal of safety research.

[45]  Zhixia Li,et al.  Modeling Reservation-Based Autonomous Intersection Control in VISSIM , 2013 .

[46]  John Hourdos,et al.  Opportunities for Preventing Rear-End Crashes: Findings from the Analysis of Actual Freeway Crash Data , 2011 .

[47]  Jaehyun So,et al.  Exploring Traffic Conflict-Based Surrogate Approach for Safety Assessment of Highway Facilities , 2015 .

[48]  A G Hobeika,et al.  ASSESSMENT OF RED LIGHT RUNNING CAMERAS IN FAIRFAX COUNTY, VIRGINIA , 2003 .

[49]  Alexander Ariza Validation of Road Safety Surrogate Measures as a Predictor of Crash Frequency Rates on a Large-scale Microsimulation Network , 2011 .

[50]  Fei Huang,et al.  Development of Traffic Safety Evaluation Method based on Simulated Conflicts at Signalized Intersections , 2013 .

[51]  Raghavan Srinivasan,et al.  Automated Enforcement: A Compendium of Worldwide Evaluations of Results , 2007 .

[52]  L Mountain,et al.  Accident prediction models for roads with minor junctions. , 1996, Accident; analysis and prevention.

[53]  Moshe Cohen,et al.  A new, non-canonical Poisson regression model for the prediction of crashes on low-volume rural roads , 2012 .

[54]  Craig Lyon,et al.  Safety Performance Functions for Signalized Intersections in Large Urban Areas: Development and Application to Evaluation of Left-Turn Priority Treatment , 2005 .

[55]  Luis F. Laracuente,et al.  Analysis of Dilemma Zone Driver Behavior at Signalized Intersections , 2007 .

[56]  Kevin Moriarty,et al.  Guidelines for Timing Yellow and Red Intervals at Signalized Intersections , 2012 .

[57]  Alireza Hadayeghi,et al.  Red Light Cameras: Surprising New Safety Results , 2010 .

[58]  Luis de Picado Santos,et al.  Safety Evaluations of Aggressive Driving on Motorways Through Microscopic Traffic Simulation and Surrogate Measures , 2012 .

[59]  Nicholas J Garber,et al.  Development of Safety Performance Functions for Two-Lane Roads Maintained by the Virginia Department of Transportation , 2010 .