Deep Reinforcement Learning Based Lane-Level Variable Speed Limit Control

Variable speed limit (VSL) is an effective traffic control method to alleviate congestion and increase safety. This paper incorporates deep reinforcement learning (DRL) into the VSL control strategy and proposes a twin delayed deep determin-istic policy gradient (TD3)-based solution. We set different speed limits between every lane to control the speed of vehicles entering the highway merging area, thereby increasing the traffic flow and improving passing efficiency. The proposed model learns a large number of discrete actions within continuous actions through the actor-critic framework, using the reward signal based on the difference between inflow and outflow to train the agent. We selected real-world road segments and collected corresponding data to test the proposed method. The simulation results show that the VSL control based on TD3 can effectively reduce average travel time and increase the number of passing vehicles.