Time Difference Penalized Traffic Signal Timing by LSTM Q-Network to Balance Safety and Capacity at Intersections

Abstract

The conflict between limited road resources and rapid car ownership makes the traffic signal timing become a pivotal challenge. Emerging studies have been carried out on adaptive signal timing, but most of them still focus on the throughput of intersections, leaving safety and travel experience unconsidered. This paper proposes a time difference penalized traffic signal timing method by reinforcement learning technique to balance safety and throughput capacity in traffic control system. Firstly, a microcosmic state representation is proposed to integrate the dynamics of both traffic lights and road vehicles, including driver behaviors of lane changing, car-following, previous phase of traffic light and its duration. Secondly, an action space, including 8 signal phases, and a behavior-aware reward function are designed to resist the red-light overflow. Finally, a partial long short term memory (LSTM) network is trained to balance traffic efficiency and traveling experience. In the network training, a parallel sampling method is adopted to obtain experience from multiple environments to accelerate the training convergence in practical application. Experimental results show that the proposed method improves the intersection efficiency up to 14.28% compared to the fixed signal timing and 5.26% compared to DQN while getting rid of red-light overflow time.

Keywords

• Traffic signal timing
• deep reinforcement learning
• DQN
• LSTM
• driving behavior
• driving safety