Research and implementation of traffic signal control based on Deep Q-Network algorithm optimization
YU Rong
ZHENG Fu
Abstract：Traffic congestion is a major challenge faced by many cities around the world.This paper aims to reduce vehicle waiting times and improve traffic flow by optimizing traffic signal control using Deep Q-Networks(DQN).The method includes modeling the traffic signal control problem as a reinforcement learning problem and using the DQN algorithm to adaptively learn and optimize the strategy.The experimental results show that the improved DQN algorithm performs excellently in reducing vehicle waiting times and improving traffic efficiency,with an overall efficiency improvement of more than 20％.The validity of the improved algorithm was verified through simulation experiments,providing a new solution for urban traffic management.
Keywords：Deep Q-Networktraffic lightsreinforcement learningoptimal control
Publication Date：2025-06-15
Online Publishing Date：2025-09-09(First online date of this platform, not the publication date of the document)
Pages：8( 134-141 )