# SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control

arXiv: https://arxiv.org/abs/2211.10871 · HTML: https://mlsanity.com/paper/2211.10871/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 50 references that are in the map.
- First posted on arXiv 20 Nov 2022.

## Builds on (most cited first, 14 of 14)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Taskonomy: Disentangling Task Transfer Learning](https://mlsanity.com/paper/1804.08328/) (2018)
- [Safe Reinforcement Learning via Shielding](https://mlsanity.com/paper/1708.08611/) (2017)
- [Residual Reinforcement Learning for Robot Control](https://mlsanity.com/paper/1812.03201/) (2018)
- [UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory](https://mlsanity.com/paper/1609.02132/) (2016)
- [Learning Phase Competition for Traffic Signal Control](https://mlsanity.com/paper/1905.04722/) (2019)
- [Safe Reinforcement Learning by Imagining the Near Future](https://mlsanity.com/paper/2202.07789/) (2022)
- [Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks](https://mlsanity.com/paper/1803.11115/) (2018)
- [Using a Deep Reinforcement Learning Agent for Traffic Signal Control](https://mlsanity.com/paper/1611.01142/) (2016)
- [Learning an Interpretable Traffic Signal Control Policy](https://mlsanity.com/paper/1912.11023/) (2019)
- [LibSignal: An Open Library for Traffic Signal Control](https://mlsanity.com/paper/2211.10649/) (2022)
- [Independent Reinforcement Learning for Weakly Cooperative Multiagent Traffic Control Problem](https://mlsanity.com/paper/2104.10917/) (2021)

## Built on by (most cited first, 15 of 15)

- [eTraM: Event-based Traffic Monitoring Dataset](https://mlsanity.com/paper/2403.19976/) (2024)
- [Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks](https://mlsanity.com/paper/2407.00286/) (2024)
- [CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control](https://mlsanity.com/paper/2405.17152/) (2024)
- [MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctions](https://mlsanity.com/paper/2408.09768/) (2024)
- [HumanLight: Incentivizing Ridesharing via Human-centric Deep Reinforcement Learning in Traffic Signal Control](https://mlsanity.com/paper/2304.03697/) (2023)
- [Safety in Traffic Management Systems: A Comprehensive Survey](https://mlsanity.com/paper/2308.06204/) (2023)
- [VLMLight: Safety-Critical Traffic Signal Control via Vision-Language Meta-Control and Dual-Branch Reasoning Architecture](https://mlsanity.com/paper/2505.19486/) (2025)
- [GPLight+: A Genetic Programming Method for Learning Symmetric Traffic Signal Control Policy](https://mlsanity.com/paper/2508.16090/) (2025)
- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025)
- [MTDT: A Multi-Task Deep Learning Digital Twin](https://mlsanity.com/paper/2405.00922/) (2024)
- [Safe Learning Control with Optimality and Stability Guarantees](https://mlsanity.com/paper/2501.15373/) (2025)
- [An Offline Meta Black-box Optimization Framework for Adaptive Design of Urban Traffic Light Management Systems](https://mlsanity.com/paper/2408.07327/) (2024)
- [Enhancing Traffic Signal Control through Model-based Reinforcement Learning and Policy Reuse](https://mlsanity.com/paper/2503.08728/) (2025)
- [Entropy-Constrained Strategy Optimization in Urban Floods: A Multi-Agent Framework with LLM and Knowledge Graph Integration](https://mlsanity.com/paper/2508.14654/) (2025)
- [CFLight: Enhancing Safety with Traffic Signal Control through Counterfactual Learning](https://mlsanity.com/paper/2512.09368/) (2025)

## Abstract (arXiv)

Traffic signal control is safety-critical for our daily life. Roughly one-quarter of road accidents in the U.S. happen at intersections due to problematic signal timing, urging the development of safety-oriented intersection control. However, existing studies on adaptive traffic signal control using reinforcement learning technologies have focused mainly on minimizing traffic delay but neglecting the potential exposure to unsafe conditions. We, for the first time, incorporate road safety standards as enforcement to ensure the safety of existing reinforcement learning methods, aiming toward operating intersections with zero collisions. We have proposed a safety-enhanced residual reinforcement learning method (SafeLight) and employed multiple optimization techniques, such as multi-objective loss function and reward shaping for better knowledge integration. Extensive experiments are conducted using both synthetic and real-world benchmark datasets. Results show that our method can significantly reduce collisions while increasing traffic mobility.