Modeling of Vehicles Merging Movement at Unsignalized Intersections Considering Drivers’ Risk Perception

In the dynamic interaction of a driver-vehicle-environment system, risk perception of drivers changes dynamically, having significant impacts on driving behavior and vehicles movement. In China, because there is less construction of stop signs, as well as limited regulation of driving courtesy, traffic operation and safety issues at unsignalized intersections require harder concern. Thus, in this study, focus was on risk perception of drivers at unsignalized intersections in China and then analysis of vehicles movement with consideration of drivers’ risk perception. A total of 150 typical merging cases were selected at an unsignalized intersection in Kunming City. On the basis of cognitive psychology theory and an adaptive neuro-fuzzy inference system, quantitative models of drivers’ risk perception were established. Drivers’ acceptable risk perception levels were identified by using a self-developed data analysis method. On the basis of game theory, the relationship among the quantitative value of drivers’ risk perception, acceptable risk perception level, and vehicle motion state was analyzed; then the vehicles merging movement model was established. Finally, the vehicles merging movement model was validated by using data collected from real-world vehicle movements and driver decisions. Results showed that the developed vehicles movement model had both high accuracy and good applicability. This study could provide theoretical and algorithmic references for the microscopic simulation and vehicle active safety control system.