A Deep Reinforcement Learning Approach to Traffic Signal Control

被引：0

作者：

Razack, Aquib Junaid ^{[1
]}

Ajith, Vysyakh ^{[1
]}

Gupta, Rajiv ^{[2
]}

机构：

[1] Birla Inst Technol & Sci, Pilani, Rajasthan, India

[2] Birla Inst Technol & Sci, Dept Civil Engn, Pilani, Rajasthan, India

来源：

2021 IEEE CONFERENCE ON TECHNOLOGIES FOR SUSTAINABILITY (SUSTECH2021) | 2021年

关键词：

Deep learning; traffic lights control; traffic management;

D O I：

10.1109/SusTech51236.2021.9467450

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Traffic Signal Control using Reinforcement Learning has been proved to have potential in alleviating traffic congestion in urban areas. Although research has been conducted in this field, it is still an open challenge to find an effective but low-cost solution to this problem. This paper presents multiple deep reinforcement learning-based traffic signal control systems that can help regulate the flow of traffic at intersections and then compares the results. The proposed systems are coupled with SUMO (Simulation of Urban MObility), an agent-based simulator that provides a realistic environment to explore the outcomes of the models.

引用

页数：7

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