As well as offering tremendous opportunities for existing transportation systems, autonomous driving presents challenges for traffic management, particularly in mixed traffic flow. The design of driving strategies for autonomous vehicles (AVs) considers both safety and efficiency to promote smooth traffic flow with no collisions. However, the complex traffic conditions at freeway bottlenecks mean that the traffic impacts of AVs in such scenarios remain unclear. Therefore, this study employs several driving strategies utilizing deep reinforcement learning (RL) to form a mixed-autonomy traffic flow on a one-way two-lane freeway with on- and off-ramps and weaving sections in SUMO traffic microsimulations. The study also examines the overall traffic impacts in terms of congested patterns, safety, efficiency, and passenger comfort. Multiple experiments are conducted with different AV driving strategies, penetration rates, and key traffic conditions. The results show that AVs with a deep-RL driving strategy significantly mitigate traffic-congested patterns and prevent the propagation of shockwaves, but at the expense of speed at low penetration rates. Moreover, the proposed driving strategy is conducive to a safer capability in vehicle dynamics. However, AVs under a deep-RL strategy lead to poorer passenger comfort at bottlenecks. Therefore, it is crucial to design a driving strategy with high robustness and scalability so that deep RL-based AVs can adapt to more complex scenarios and traffic events. Overall, the present research improves the understandings of AV driving mechanisms and provides insights into corresponding future traffic-management measures.

One of the most important goals of cooperative driving is to control connected automated vehicles (CAVs) passing through conflict areas safely and efficiently without traffic signals. As a typical application scenario, allocating right‐of‐way reasonably at unsignalized intersections can effectively avoid collisions and reduce traffic delays. Proposed here is a new cooperative driving strategy for CAVs at unsignalized intersections based on distributed Monte Carlo tree search (MCTS). A task‐area partition framework is also proposed to decompose the mission of cooperative driving into three main tasks: vehicle information sharing, passing order optimization, and trajectory control. Based on the schedule tree of the vehicle passing order, the root parallelization of MCTS combined with the majority voting rule is used to explore as many feasible passing orders (leaf nodes) as possible in a distributed way and find a nearly global‐optimal passing order within the limited planning time. The aim is for CAVs to perform proper trajectory adjustments based on the obtained passing order to minimize traffic delays while making the slightest acceleration adjustments. A coupled simulation platform integrating SUMO and Python is developed to construct the unsignalized intersection scenarios and generate the proposed distributed cooperative driving strategy. Comparative analysis with conventional driving strategies demonstrates that the proposed strategy significantly enhances efficiency, safety, comfort, and emission, aligning well with innovative and environmentally friendly urban mobility aspirations.

With the rapid development of urban rail transit, the scientific assurance of pedestrian safety has become an important issue. Orderly behavior is a crucial factor affecting pedestrian safety. Therefore, in-depth research into pedestrian behavior is needed. This study carries out an evaluation of safety in pedestrian flows by establishing a new force model based on real-time data. In this model, we consider the microscopic characteristics of pedestrians and define four force influence mechanisms for simulating pedestrian behavior. Compared with existing models, this model incorporates partial impact dynamics to make it applicable to the particular environment of subway stations. Through the validation of real-world data, it is demonstrated that the model can accurately describe pedestrian behavior and better reproduce the characteristics of pedestrians. The influence of pedestrians and of environmental factors on the model are also discussed. Using our model, we propose a risk evaluation system based on pedestrian volatility. By using real-time pedestrian information from subway stations, the potential risk to pedestrians can be discerned and assessed in advance. This research advances the management of pedestrian safety and provides a framework for studying behavior models and for safety evaluation.

Autonomous driving technologies offer considerable opportunities for smarter and more-effective intelligent transportation systems, but they also pose unprecedented challenges that are likely to have various as-yet uncertain and unclear impacts on the operational characteristics of traffic flow. Therefore, evaluating how mixed-autonomy traffic affects traffic systems is important for learning new strategies for balancing traffic demand and supply. In the present study, an urban scenario with signalized intersection networks is selected, and simulations of mixed-autonomy traffic with different traffic demands and penetration rates are conducted in SUMO traffic microsimulations. A proximal-policy-optimization algorithm with deep reinforcement learning is used to propose multi-objective control strategies for autonomous driving aimed at improved safety, driving efficiency, and passenger comfort. The results show that the designed control strategies perform excellently with fast and stable convergence, guiding the autonomous vehicle actively to optimize the traffic operation and providing different driving preferences. Specifically, the aggressive strategy benefits from efficient driving, and the conservative ones perform well regarding comfort and ecology. Also, the strategies are applicable for different traffic demands and penetration rates, where high penetration of autonomous driving offers significant improvements in driving efficiency and stability.