DataLight: Offline Data-Driven Traffic Signal Control
This addresses traffic management for urban planners by offering a safer, data-efficient solution, though it appears incremental as it builds on existing RL methods with offline adaptations.
The paper tackles traffic signal control by introducing DataLight, an offline data-driven approach that avoids real-world safety risks, achieving superior performance compared to state-of-the-art online and offline methods.
Reinforcement learning (RL) has emerged as a promising solution for addressing traffic signal control (TSC) challenges. While most RL-based TSC systems typically employ an online approach, facilitating frequent active interaction with the environment, learning such strategies in the real world is impractical due to safety and risk concerns. To tackle these challenges, this study introduces an innovative offline data-driven approach, called DataLight. DataLight employs effective state representations and reward function by capturing vehicular speed information within the environment. It then segments roads to capture spatial information and further enhances the spatially segmented state representations with sequential modeling. The experimental results demonstrate the effectiveness of DataLight, showcasing superior performance compared to both state-of-the-art online and offline TSC methods. Additionally, DataLight exhibits robust learning capabilities concerning real-world deployment issues. The code is available at https://github.com/LiangZhang1996/DataLight.