Deep Reinforcement Learning for Adaptive Traffic Signal Control
This work addresses the challenge of adaptive traffic signal control for urban traffic management, offering a more deployable solution, though it is incremental as it builds on existing DRL approaches.
The paper tackles the problem of optimizing traffic signal control by proposing a deep reinforcement learning framework that explicitly considers realistic traffic scenarios and constraints, resulting in significantly improved traffic performance compared to baseline controllers.
Many existing traffic signal controllers are either simple adaptive controllers based on sensors placed around traffic intersections, or optimized by traffic engineers on a fixed schedule. Optimizing traffic controllers is time consuming and usually require experienced traffic engineers. Recent research has demonstrated the potential of using deep reinforcement learning (DRL) in this context. However, most of the studies do not consider realistic settings that could seamlessly transition into deployment. In this paper, we propose a DRL-based adaptive traffic signal control framework that explicitly considers realistic traffic scenarios, sensors, and physical constraints. In this framework, we also propose a novel reward function that shows significantly improved traffic performance compared to the typical baseline pre-timed and fully-actuated traffic signals controllers. The framework is implemented and validated on a simulation platform emulating real-life traffic scenarios and sensor data streams.