Variable Speed Limit Control for Mixed Traffic Flow on Highways Based on Deep-Reinforcement Learning

作者：Heyao Gao, Hongfei Jia, Ruiyi Wu, Qiuyang Huang, Jingjing Tian, Chao Liu, Xiaochao Wang · 发表于：Journal of Transportation Engineering Part A Systems · 年份：2023 · DOI：10.1061/jtepbs.teeng-8116 · 被引用次数：8 · 研究领域：Traffic control and management、Traffic Prediction and Management Techniques、Transportation Planning and Optimization

With the development of autonomous driving, a mixed traffic flow state composed of connected automated vehicles (CAVs) and human-driven vehicles (HVs) will last for an extended period. The abundant computing resources and CAVs with high compliance in the intelligent connected environment provide a good situation for variable speed limit control on highways, which helps even the traffic flow and improves traffic efficiency and safety. In this paper, we propose a variable speed limit control method for mixed traffic flow based on deep-reinforcement learning. First, the variable speed limit control problem is abstracted into a Markov decision process and the factors of real-time CAV penetration rates and predictions are considered in the state description. Different from variable message signs (VMS), CAVs are taken as the executive objects of the controller so that the variable speed limit control for mixed traffic flow is realized indirectly through the interaction with HVs. Next, double deep Q network (DDQN) is introduced to calculate the optimal speed limit in different states. Finally, the empirical study on US101-S proves the effectiveness of the proposed model. The results show that the variable speed limit control model based on the DDQN algorithm can effectively improve the efficiency and environmental benefits of mixed traffic flow. Moreover, the multi-objective reward function can achieve a better control effect than the single objective. Besides, the proposed model ou...