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Logit-Based Analysis of Drivers' Crossing Behavior at Unsignalized Intersections in China
Guangquan Lu1, Miaomiao Liu2, Yunpeng Wang3
1Beihang University, Beijing, ChinaKunming University of Science and Technology, Kunming, ChinaBeihang University, Beijing, China lugq@buaa.edu.cn.
Objective:
This study aims to investigate the crossing behavior of straight-moving drivers when they encounter other straight-moving drivers at unsignalized intersections in China.
Background:
In China, when two vehicle drivers encounter at an unsignalized intersection, neither driver completely stops his or her vehicle in most cases. Instead, one driver gradually approaches the intersection and dynamically decides to either yield or preempt by gaming with the other vehicle. This process increases the probability of accidents.
Method:
A total of 305 crossing cases were collected at an unsignalized intersection in Kunming City. Motion parameters were extracted from the video detection program designed by our research group. Based on a logistic regression method, we analyzed decision making moment of straight-moving drivers under crossing conditions, established crossing behavior models, and identified the main factors that affected drivers' decisions. In all, 68 cases observed at a separate intersection were used to validate the established models.
Results:
For crossing processes at unsignalized intersections in China, straight-moving drivers from the right side completed preemptive/yielding decisions at 1.3 s before reaching the crossing point. However, the majority of straight-moving drivers from the left side completed decisions at 1.1 s before reaching the crossing point. The most important parameter that influenced the drivers' decisions was the difference between the speeds of the two vehicles.
Conclusion:
An effective method for preventing traffic conflict between two straight-moving drivers is to control the speeds of the vehicles before they enter the intersection.
Application:
This study explores crossing behavior of straight-moving drivers and provides significant insights for controlling driver behavior.
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