Yu Qian’s research while affiliated with Southeast University and other places

With the development of vehicle-road network technologies, the future traffic flow will appear in the form of hybrid network traffic flow for a long time. Due to the change in traffic characteristics, the current hard shoulder running strategy based on traditional traffic characteristics cannot effectively serve the hybrid network traffic flow scenario, and will even lead to the further deterioration of traffic congestion. In order to propose a hard shoulder running strategy suitable for a hybrid network environment, a traffic breakdown prediction method based on a hidden Markov model was established. Secondly, the characteristics of traffic breakdown in a hybrid network environment were analyzed. Finally, based on the traffic breakdown characteristics in a hybrid network environment, a dynamic hard shoulder running method based on the hidden Markov model was proposed. The effectiveness of HMMD-HSR was verified by simulation and comparison with HMM-HSR, LMD-HSR, and N-HSR. The simulation results show that the HMMD-HSR proposed in this paper can improve operation efficiency and reduce travel time in a congested expressway.

The emergence and application of connected and automated vehicles (CAVs) have played a positive role in improving the efficiency of urban transportation and achieving sustainable development. To improve the traffic efficiency at signalized intersections in a connected environment while simultaneously reducing energy consumption and ensuring a more comfortable driving experience, this study investigates a flexible and real-time control method to navigate the CAVs at signalized intersections utilizing reinforcement learning (RL). Initially, control of CAVs at intersections is formulated as a Markov Decision Process (MDP) based on the vehicles’ motion state and the intersection environment. Subsequently, a comprehensive reward function is formulated considering energy consumption, efficiency, comfort, and safety. Then, based on the established environment and the twin delayed deep deterministic policy gradient (TD3) algorithm, a control algorithm for CAVs is designed. Finally, a simulation study is conducted using SUMO, with Lankershim Boulevard as the research scenario. Results indicate that the proposed methods yield a 13.77% reduction in energy consumption and a notable 18.26% decrease in travel time. Vehicles controlled by the proposed method also exhibit smoother driving trajectories.