Modeling and simulation of approaching behaviors to signalized intersections based on risk quantification

• A risk field model independent on yellow duration is proposed to quantify the risk constraints of traffic lights on vehicle movement. • A driving behavior model framework is established to explain the approaching behaviors to signalized intersections from the perspective of human behavioral mechanism. • By considering drivers’ desired risks, the probability of passing the stop line during yellow period is obtained by simulation and compared with that calculated by existing models. • Considerations regarding the superiority of modeling based on human behavioral mechanisms compared to data-driven modeling are presented. The stop/go decisions made by drivers who are approaching signalized intersections during yellow period will affect the safety and efficiency of intersections. Existing research mostly modeled drivers’ decision-making behaviors using real-world driving data, while these datasets were collected in different traffic flows and road environments, and it is difficult to develop models suitable for different intersections. Aiming at explaining the approaching behaviors to signalized intersections from the perspective of human behavioral mechanism, this study establishes a driving behavior model framework, including a risk field model of dynamic traffic control elements independent on yellow duration, and a trajectory planning model constructed according to the risk homeostasis theory and preview-follower theory. Probabilities of passing the stop line during yellow period and the distribution of acceleration and deceleration rates when passing are obtained in the simulation by the Monte Carlo method. Results show the validity of the proposed model and its applicability to drivers with different desired risks. Compared to the proposed model, drivers are more inclined to use smaller acceleration rates or greater deceleration rates when entering intersections in observed cases. The intervention of reaction time may decrease the probabilities of passing. This study is an indispensable supplement to our previous study, contributing a unified model based on risk quantification to comprehensively describe the risk of the traffic environment, and is an attempt to promote the development of driving behavior models.