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.

翻译：全球每年因交通事故导致数百万人伤亡，其中大量事故发生在交叉路口。交通信号控制（TSC）是提升城市交叉路口安全性的有效策略。尽管强化学习（RL）方法在优化TSC中日益普及，但这些方法通常优先考虑通行效率而非安全性，未能解决二者间的关键平衡问题。此外，这些方法通常缺乏可解释性。反事实（CF）学习是因果分析领域中一种前景广阔的方法。本研究提出一种新颖的框架，以改进TSC中安全导向的强化学习。该框架引入基于CF学习的新方法，旨在回答以下问题：“当不安全事件发生时，若回溯执行替代动作，该不安全事件在后续时段是否仍会发生？”为解答此问题，我们提出一种新的结构因果模型来预测执行不同动作后的结果，并设计了一个与附加“X”模块集成的CF模块，以促进安全强化学习实践。基于此框架衍生的新算法CFLight，通过近似零碰撞控制策略，有效应对复杂安全事件，显著提升交叉路口安全性。通过在真实场景与合成数据集上的大量数值实验，我们证明相较于传统RL方法及近期安全RL模型，CFLight能减少碰撞并提升整体交通性能。此外，本方法构建了一个通用且安全的RL框架，为其他领域的应用提供了可能性。数据与代码已发布于GitHub：https://github.com/AdvancedAI-ComplexSystem/SmartCity/tree/main/CFLight。