# A Reinforcement Learning Approach for Intelligent Traffic Signal Control at Urban Intersections

arXiv: https://arxiv.org/abs/1905.07698 · HTML: https://mlsanity.com/paper/1905.07698/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2019.

## Built on by (most cited first, 6 of 6)

- [The Real Deal: A Review of Challenges and Opportunities in Moving Reinforcement Learning-Based Traffic Signal Control Systems Towards Reality](https://mlsanity.com/paper/2206.11996/) (2022)
- [Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline](https://mlsanity.com/paper/2101.09640/) (2021)
- [Adaptive traffic signal control optimization using a novel road partition and multi-channel state representation method](https://mlsanity.com/paper/2602.12296/) (2026)
- [Multi-Vehicle Trajectory Prediction at Intersections using State and Intention Information](https://mlsanity.com/paper/2301.02561/) (2023)
- [Traffic Co-Simulation Framework Empowered by Infrastructure Camera Sensing and Reinforcement Learning](https://mlsanity.com/paper/2412.03925/) (2024)
- [CFLight: Enhancing Safety with Traffic Signal Control through Counterfactual Learning](https://mlsanity.com/paper/2512.09368/) (2025)

## Abstract (arXiv)

Ineffective and inflexible traffic signal control at urban intersections can often lead to bottlenecks in traffic flows and cause congestion, delay, and environmental problems. How to manage traffic smartly by intelligent signal control is a significant challenge in urban traffic management. With recent advances in machine learning, especially reinforcement learning (RL), traffic signal control using advanced machine learning techniques represents a promising solution to tackle this problem. In this paper, we propose a RL approach for traffic signal control at urban intersections. Specifically, we use neural networks as Q-function approximator (a.k.a. Q-network) to deal with the complex traffic signal control problem where the state space is large and the action space can be discrete. The state space is defined based on real-time traffic information, i.e. vehicle position, direction and speed. The action space includes various traffic signal phases which are critical in generating a reasonable and realistic control mechanism, given the prominent spatial-temporal characteristics of urban traffic. In the simulation experiment, we use SUMO, an open source traffic simulator, to construct realistic urban intersection settings. Moreover, we use different traffic patterns, such as major/minor road traffic, through/left-turn lane traffic, tidal traffic, and varying demand traffic, to train a generalized traffic signal control model that can be adapted to various traffic conditions. The simulation results demonstrate the convergence and generalization performance of our RL approach as well as its significant benefits in terms of queue length and wait time over several benchmarking methods in traffic signal control.