A Multiobjective Dynamic Weight Allocation Model for Lane-Level VSL Control Using Deep Reinforcement Learning in Mixed Traffic

As an important control method in the expressway weaving area, variable speed limit (VSL) effectively regulates the dynamic distribution of traffic flow to alleviate congestion. However, given different traffic demand conditions, the control objectives should be different. How to dynamically adjust the evaluation index weights when implementing VSL control according to traffic flow demand is particularly important. For this reason, this paper proposes a multiobjective dynamic weight allocation model and lane-level variable speed limit (LVSL) method by introducing a deep reinforcement learning (DRL) algorithm. First, LVSL control of traffic flow is modeled as a Markov decision process (MDP), and a comprehensive reward function considering traffic efficiency, safety, and environmental benefits is constructed on the scenario of weaving areas with multiple lanes. Second, a multiobjective dynamic weight allocation model and an LVSL (DW-DDPGLVSL) control method based on the deep deterministic policy gradient (DDPG) algorithm are prompted. Finally, simulation tests are conducted using real-world network data, and the results show that the proposed method can improve the safety, efficiency, and environmental friendliness of expressways.