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

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