MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctions

TL;DR

This paper introduces MalLight, a reinforcement learning-based framework that optimizes traffic signal control during malfunctions, significantly reducing congestion and improving safety at affected intersections.

Contribution

It presents a novel RL-based approach with influence-aware modules for coordinated traffic signal control during malfunctions, addressing a gap in existing traffic management research.

Findings

Achieves up to 48.6% reduction in throughput loss during malfunctions

Demonstrates superior performance over conventional and deep learning methods

Validates effectiveness on real-world traffic datasets

Abstract

Urban traffic is subject to disruptions that cause extended waiting time and safety issues at signalized intersections. While numerous studies have addressed the issue of intelligent traffic systems in the context of various disturbances, traffic signal malfunction, a common real-world occurrence with significant repercussions, has received comparatively limited attention. The primary objective of this research is to mitigate the adverse effects of traffic signal malfunction, such as traffic congestion and collision, by optimizing the control of neighboring functioning signals. To achieve this goal, this paper presents a novel traffic signal control framework (MalLight), which leverages an Influence-aware State Aggregation Module (ISAM) and an Influence-aware Reward Aggregation Module (IRAM) to achieve coordinated control of surrounding traffic signals. To the best of our knowledge, this study pioneers the application of a Reinforcement Learning(RL)-based approach to address the challenges posed by traffic signal malfunction. Empirical investigations conducted on real-world datasets substantiate the superior performance of our proposed methodology over conventional and deep learning-based alternatives in the presence of signal malfunction, with reduction of throughput alleviated by as much as 48.6$\%$.