CFLight: Enhancing Safety with Traffic Signal Control through Counterfactual Learning

TL;DR

CFLight introduces a counterfactual learning framework to enhance safety in traffic signal control by predicting and preventing unsafe events, significantly reducing collisions while maintaining traffic efficiency.

Contribution

This paper presents a novel counterfactual learning-based algorithm, CFLight, that improves safety in traffic signal control by addressing unsafe events through causal modeling and safe reinforcement learning.

Findings

CFLight reduces traffic collisions significantly.

It outperforms conventional and safe RL methods in safety and traffic performance.

The framework is generalizable to other domains.

Abstract

Traffic accidents result in millions of injuries and fatalities globally, with a significant number occurring at intersections each year. Traffic Signal Control (TSC) is an effective strategy for enhancing safety at these urban junctures. Despite the growing popularity of Reinforcement Learning (RL) methods in optimizing TSC, these methods often prioritize driving efficiency over safety, thus failing to address the critical balance between these two aspects. Additionally, these methods usually need more interpretability. CounterFactual (CF) learning is a promising approach for various causal analysis fields. In this study, we introduce a novel framework to improve RL for safety aspects in TSC. This framework introduces a novel method based on CF learning to address the question: ``What if, when an unsafe event occurs, we backtrack to perform alternative actions, and will this unsafe event still occur in the subsequent period?'' To answer this question, we propose a new structure causal model to predict the result after executing different actions, and we propose a new CF module that integrates with additional ``X'' modules to promote safe RL practices. Our new algorithm, CFLight, which is derived from this framework, effectively tackles challenging safety events and significantly improves safety at intersections through a near-zero collision control strategy. Through extensive numerical experiments on both real-world and synthetic datasets, we demonstrate that CFLight reduces collisions and improves overall traffic performance compared to conventional RL methods and the recent safe RL model. Moreover, our method represents a generalized and safe framework for RL methods, opening possibilities for applications in other domains. The data and code are available in the github https://github.com/AdvancedAI-ComplexSystem/SmartCity/tree/main/CFLight.