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/MJLee00/CFLight-Enhancing-Safety-with-Traffic-Signal-Control-through-Counterfactual-Learning.

翻译：交通事故在全球范围内导致数百万伤亡，其中每年有大量事故发生在交叉路口。交通信号控制（TSC）是提升这些城市节点安全性的有效策略。尽管强化学习（RL）方法在优化TSC中日益流行，但这些方法往往优先考虑通行效率而非安全性，因此未能解决这两方面之间的关键平衡。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。在本研究中，我们引入了一个新颖框架，以改进TSC中安全方面的强化学习。该框架提出了一种基于CF学习的新方法，以回答以下问题：“当不安全事件发生时，如果我们回溯执行替代行动，该不安全事件在后续时段是否仍会发生？”为回答此问题，我们提出了一个新的结构因果模型来预测执行不同行动后的结果，并设计了一个新的CF模块，该模块与附加的“X”模块集成，以促进安全的RL实践。我们基于此框架提出的新算法CFLight，通过近乎零碰撞的控制策略，有效应对具有挑战性的安全事件，显著提升了交叉路口的安全性。通过在真实世界和合成数据集上进行的大量数值实验，我们证明CFLight相较于传统RL方法及近期的安全RL模型，减少了碰撞并提升了整体交通性能。此外，我们的方法代表了一种通用且安全的RL框架，为其他领域的应用开辟了可能性。数据和代码可在GitHub仓库https://github.com/MJLee00/CFLight-Enhancing-Safety-with-Traffic-Signal-Control-through-Counterfactual-Learning获取。