Classification analysis of driver's stop/go decision and red-light running violation.

When the driver encounters a signal change from green to yellow, he is required to make a stop or go decision based on his speed and the distance to the stop bar. Making the wrong decision will lead to a red-light running violation or an abrupt stop at the intersection. In this study, a field data collection was conducted at a high-speed signalized intersection, where a video-based system with three cameras was used to record the drivers' behavior related to the onset of yellow. Observed data include drivers' stop/go decisions, red-light running violation, lane position in the highway, positions (leading/following) in the traffic flow, vehicle type, and vehicles' yellow-onset speeds and distances from the intersection. Further, classification tree models were applied to analyze how the probabilities of a stop or go decision and of red-light running are associated with the traffic parameters. The data analysis indicated that vehicle's distance from the intersection at the onset of yellow, operating speed, and position in the traffic flow are the most important predictors for both the stop/go decision and red-light running violation. This study illustrates that the tree models are helpful to recognize and predict how drivers make stop/go decisions and partake in red-light running violations corresponding to the traffic parameters.

[1]  Mary Pat McKay.  National Highway Traffic Safety Administration (NHTSA) notes. Seat belt use in 2005: demographic results. , 2006, Annals of emergency medicine.

[2]  C Newton,et al.  Evaluation of an alternative traffic light change anticipation system. , 1997, Accident; analysis and prevention.

[3]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[4]  James A Bonneson,et al.  Intersection Safety at High-Speed Signalized Intersections: Number of Vehicles in Dilemma Zone as Potential Measure , 2004 .

[5]  James A Bonneson,et al.  TRAFFIC DATA COLLECTION USING VIDEO-BASED SYSTEMS , 1995 .

[6]  Myung-Soon Chang,et al.  TIMING TRAFFIC SIGNAL CHANGE INTERVALS BASED ON DRIVER BEHAVIOR , 1985 .

[7]  Christopher K Strong,et al.  Collecting Vehicle-Speed Data by Using Time-Lapse Video Recording Equipment , 2003 .

[8]  Srinivasa R Sunkari,et al.  Performance of advance warning for end of green system for high-speed signalized intersections , 2005 .

[9]  Essam Radwan,et al.  Effect of a Pavement Marking Countermeasure on Improving Signalized Intersection Safety , 2007 .

[10]  Peter S Parsonson.  SIGNALIZATION OF HIGH-SPEED, ISOLATED INTERSECTIONS , 1978 .

[11]  Allan F. Williams,et al.  Changes in crash risk following re-timing of traffic signal change intervals. , 2002, Accident; analysis and prevention.

[12]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[13]  Mohamed Abdel-Aty,et al.  Identification of Intersections' Crash Profiles/Patterns , 2006 .

[14]  Bryan E. Porter,et al.  Predicting Red-Light Running Behavior: A Traffic Safety Study in Three Urban Settings , 2000 .

[15]  H Köll,et al.  Driver behaviour during flashing green before amber: a comparative study. , 2004, Accident Analysis and Prevention.

[16]  Yosef Sheffi,et al.  A Model of Driver Behavior at High Speed Signalized Intersections , 1981 .

[17]  Panagiotis Papaioannou,et al.  Driver behaviour, dilemma zone and safety effects at urban signalized intersections in Greece. , 2007, Accident; analysis and prevention.

[18]  Cj Baguley.  'Running the red' at signals on high-speed roads , 1988 .

[19]  David Mahalel,et al.  A BEHAVIORAL APPROACH TO RISK ESTIMATION OF REAR-END COLLISIONS AT SIGNALIZED INTERSECTIONS , 1987 .