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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. [ABSTRACT FROM AUTHOR]

• 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. [ABSTRACT FROM AUTHOR]

It has been previously documented that under the condition of crossing an uncontrolled intersection, the decision-making process of drivers is rather complex and highly interactive: they need to decide about the timing, direction, and strategy to complete the required maneuver to avoid potential conflicts. Given the significant impact of this behavior on intersection safety and capacity, we model the merging behavior of a straight-moving vehicle facing a turning vehicle at an uncontrolled intersection in China. We expect that this model can predict the probability that a straight-moving driver has a preemptive status (i.e., arrive at the merging point before the turning vehicle). The factors which determine the drivers' decision behavior are analyzed, and then we propose a logistic regression model using a dataset collected from an uncontrolled intersection in Kunming, China. Our model reveals that the speed difference, the distance between the two vehicles, and the distance of the turning vehicle to the merging point are the major determinants of a straight-moving driver's decision. In addition, the prediction results from our model are compared with real-world observations, and better validate the decision behavior model in China. [ABSTRACT FROM AUTHOR]

Highlights: [•] The merging process at unsignalized intersection in China is illustrated. [•] We use classification tree method to analyze drivers’ dynamic decision making. [•] We indentify the motion parameters which influence drivers’ decision significantly. [•] Drivers finish decisions between 1.3 and 1.5s before reaching the merging point. [•] This study suggests effective way to avoid traffic conflicts. [ABSTRACT FROM AUTHOR]

Highlights: [•] The crossing process at unsignalized intersections in China is illustrated. [•] We use classification tree method to analyze drivers’ decision making. [•] We indentify the motion parameters which influence drivers’ decision significantly. [•] This study sheds lights on drivers’ decision model suited for China. [Copyright &y& Elsevier]

• An effective traffic organization method is proposed for CAVs in road networks. • An effective iterative adjustment strategy is used to optimize trajectories. • A novel composite road planning strategy is designed for different demands. • The proposed method ensures safe, efficient, energy-saving, and comfortable trips. Connected automated vehicles (CAVs) have been currently considered as promising solutions for realization of envisioned autonomous traffic management systems in the future. CAVs can achieve high desired traffic efficiency and provide safe, energy-saving, and comfortable ride experience for passengers. However, in order to practically implement such autonomous systems based on CAVs, there exist several significant challenges to be dealt with, such as coupled spatiotemporal constraints on CAVs' trajectories at unsignalized intersections, multiple objectives for trajectory optimization in road segments, and heterogeneous decision-making behaviors of CAVs in road networks with highly dynamic traffic demand. In this paper, we propose a cooperative autonomous traffic organization method for CAVs in multi-intersection road networks. The methodological framework consists of threefold components: an autonomous crossing strategy based on a conflict resolution approach at unsignalized intersections, multi-objective trajectory optimization in road segments, and a composite strategy for route planning considering heterogeneous decision-making behaviors of CAVs based on social and individual benefit, respectively. Specifically, we first identify a set of potential conflict points of different CAVs' spatial trajectories at the intersection, and then design different minimum safe time headways to resolve conflicts. Under the constraints of entry and exit conditions at adjacent intersections, we propose a multi-objective optimal control model by jointly considering vehicle safety, energy conservation, and ride comfort, and then analytically derive a closed-form solution for optimizing the CAVs' trajectories. Furthermore, with the purpose to adapt dynamic traffic demand, we propose a composite strategy for route planning by coordinating heterogeneous decision-making behaviors of CAVs in road networks. Finally, extensive simulation experiments have been performed to validate our proposed method and to demonstrate its advantage over conventional baseline schemes in terms of global traffic efficiency. Additional numerical results are also provided to shed light on the impact of the proportion of CAVs with heterogeneous decision-making behaviors on the global system performance. [ABSTRACT FROM AUTHOR]

Studying driver awareness of information, particularly risk perception, is vital to understanding driving behavior and improving traffic safety. In the dynamic interaction of a driver-vehicle-environment system, risk perception of drivers changes dynamically. In this study, we focused on drivers’ risk perception at unsignalized intersections in China and analyzed their crossing behavior with consideration of risk perception. Based on cognitive psychology theory and an adaptive neuro-fuzzy inference system, quantitative models of drivers’ risk perception were established for the crossing processes between two straight-moving vehicles from the orthogonal direction. Drivers’ acceptable risk perception levels were identified 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 crossing behavior models of drivers were established. Finally, the behavior models were validated using data collected from real-world vehicle movements and driver decisions. The results showed that the developed behavior models had both high accuracy and good applicability. This study would provide theoretical and algorithmic references for the microscopic simulation and active safety control system of vehicles. [ABSTRACT FROM AUTHOR]