DataLight: Offline Data-Driven Traffic Signal Control

TL;DR

DataLight is an offline, data-driven traffic signal control method that uses spatial and sequential modeling of vehicular speeds to outperform existing online and offline approaches, enabling safer real-world deployment.

Contribution

Introduces DataLight, an offline RL-based traffic signal control framework that effectively models spatial and sequential data for improved performance.

Findings

Outperforms state-of-the-art online and offline TSC methods

Demonstrates robustness in real-world deployment scenarios

Utilizes vehicular speed data for enhanced decision-making

Abstract

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.