Application of Multi-Agent Deep Reinforcement Learning to Optimize Real-World Traffic Signal Controls
preprintposted on 06.12.2021, 14:36 by Maxim FriesenMaxim Friesen, Tian Tan, Jürgen Jasperneite, Jie Wang
Increasing traffic congestion leads to significant costs associated by additional travel delays, whereby poorly configured signaled intersections are a common bottleneck and root cause. Traditional traffic signal control (TSC) systems employ rule-based or heuristic methods to decide signal timings, while adaptive TSC solutions utilize a traffic-actuated control logic to increase their adaptability to real-time traffic changes. However, such systems are expensive to deploy and are often not flexible enough to adequately adapt to the volatility of today's traffic dynamics. More recently, this problem became a frontier topic in the domain of deep reinforcement learning (DRL) and enabled the development of multi-agent DRL approaches that could operate in environments with several agents present, such as traffic systems with multiple signaled intersections. However, most of these proposed approaches were validated using artificial traffic grids. This paper therefore presents a case study, where real-world traffic data from the town of Lemgo in Germany is used to create a realistic road model within VISSIM. A multi-agent DRL setup, comprising multiple independent deep Q-networks, is applied to the simulated traffic network. Traditional rule-based signal controls, currently employed in the real world at the studied intersections, are integrated in the traffic model with LISA+ and serve as a performance baseline. Our performance evaluation indicates a significant reduction of traffic congestion when using the RL-based signal control policy over the conventional TSC approach in LISA+. Consequently, this paper reinforces the applicability of RL concepts in the domain of TSC engineering by employing a highly realistic traffic model.