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

TL;DR

This paper presents a reinforcement learning method using neural networks for adaptive traffic signal control at urban intersections, demonstrating improved traffic flow management in simulations.

Contribution

It introduces a neural network-based RL approach for complex traffic signal control, handling large state and action spaces with real-time traffic data.

Findings

Significant reduction in queue length and wait time.

Effective convergence and generalization across various traffic patterns.

Outperforms benchmark methods in simulation experiments.

Abstract

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.