Preempt or yield? An analysis of driver's dynamic decision making at unsignalized intersections by classification tree

Abstract For developing countries and regions, due to less construction of stop signs and roundabouts, as well as limited regulation of driving courtesy, safety issues at unsignalized intersections require harder concern. In China, drivers rarely stop completely at unsignalized intersections, but gradually enter and dynamically make their decisions to yield or preempt by gaming with other vehicles. Wrong decisions made in this quick process often lead to accidents. In this study, we aimed to explore how straight drivers dynamically made decisions when encountered merging vehicles at unsignalized intersections in China. By video graphing traffic scenarios, 150 cases of merging traffic were selected at a 4-legged unsignalized intersection in Kunming City. Motion parameters of the vehicles were extracted from video detection software. By modeling the motion parameters to a classification tree, the decision moment of straight drivers’ yielding/preemptive decision and the motion parameters which influenced drivers’ decision significantly were identified. Results showed that straight drivers made yielding/preemptive decisions 1.3–1.5 s before reaching the merging point. Speed difference between the straight vehicle and the turning vehicle was the most important factor to impact straight driver’s decision-making. Turning vehicle’s speed and distance to the merging point also impacted straight driver’s decision. Moreover, a U-shape curve was found when plotted the minimum gap between the two vehicles by the speed difference of the two vehicles at the decision moment (1.3 s). The accurate motion parameters found in this study helped to develop driver’s thorough behavior model at unsignalized intersections, and suggest safety measures further.

[1]  China. State Statistical Bureau,et al.  Statistical yearbook of China , 1985 .

[2]  Xuedong Yan,et al.  Analyses of Rear-End Crashes Based on Classification Tree Models , 2006, Traffic injury prevention.

[3]  Juan de Oña,et al.  A classification tree approach to identify key factors of transit service quality , 2012, Expert Syst. Appl..

[4]  Bruce Robinson,et al.  CAPACITY AND LEVEL OF SERVICE AT UNSIGNALIZED INTERSECTIONS. FINAL REPORT. VOLUME 2 - ALL-WAY STOP-CONTROLLED INTERSECTIONS , 1996 .

[5]  Hesham A. Rakha,et al.  Analysis of Brake Perception-Reaction Times on High-Speed Signalized Intersection Approaches , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[6]  Yang Xin-yue Study on Decision Mechanism of Driving Behavior Based on Decision Tree , 2008 .

[7]  Werner Brilon,et al.  Unsignalized Intersections - A Third Method for Analysis , 2002 .

[8]  Moshe Livneh,et al.  A decision model for gap acceptance and capacity at intersections , 2002 .

[9]  Samer Madanat,et al.  PROBABILISTIC DELAY MODEL AT STOP-CONTROLLED INTERSECTION , 1994 .

[10]  Chaohsin Lin,et al.  Using neural networks as a support tool in the decision making for insurance industry , 2009, Expert Syst. Appl..

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

[12]  Qiang Meng,et al.  Modeling speed-flow relationship and merging behavior in work zone merging areas , 2011 .

[13]  Yihu Wu,et al.  A Study on Reaction Time Distribution of Group Drivers at Car-Following , 2009, 2009 Second International Conference on Intelligent Computation Technology and Automation.

[14]  Ali S Abbany,et al.  Modeling aggressive driver behavior at unsignalized intersections. , 2007, Accident; analysis and prevention.

[15]  Rune Elvik,et al.  The Handbook of Road Safety Measures , 2009 .

[16]  Joewono Prasetijo,et al.  Capacity Analysis of Unsignalized Intersection Under Mixed Traffic Conditions , 2012 .

[17]  Said M. Easa,et al.  DISAGGREGATE GAP-ACCEPTANCE MODEL FOR UNSIGNALIZED T-INTERSECTIONS. , 1997 .

[18]  Li-Yen Chang,et al.  Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model , 2013 .

[19]  E. Boer Car following from the driver’s perspective , 1999 .

[20]  S. Washington,et al.  Statistical and Econometric Methods for Transportation Data Analysis , 2010 .

[21]  P. Dheena,et al.  Multicriteria decision-making combining fuzzy set theory, ideal and anti-ideal points for location site selection , 2011, Expert Syst. Appl..

[22]  Dongsong Zhang,et al.  Predicting and explaining patronage behavior toward web and traditional stores using neural networks: a comparative analysis with logistic regression , 2006, Decis. Support Syst..

[23]  T Troutbeck,et al.  LIMITED PRIORITY MERGE AT UNSIGNALIZED INTERSECTIONS , 1997 .

[24]  Daxin Tian,et al.  Quantifying the Severity of Traffic Conflict by Assuming Moving Elements as Points in Intersection , 2011 .

[25]  Rui-jun Guo,et al.  Gap Acceptance at Priority-Controlled Intersections , 2011 .

[26]  Yuan-Liang Su,et al.  The impact of expert-system-based training on calibration of decision confidence in emergency management , 1998 .

[27]  Wang Zhong,et al.  Research on the Driver Reaction Time of Safety Distance Model on Highway Based on Fuzzy Mathematics , 2010, 2010 International Conference on Optoelectronics and Image Processing.

[28]  Xuedong Yan,et al.  Classification analysis of driver's stop/go decision and red-light running violation. , 2010, Accident; analysis and prevention.