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

共 50 条

[21] A Comparison of Deep Reinforcement Learning Models for Isolated Traffic Signal Control
Mao, Feng
Li, Zhiheng
Li, Li
[J]. IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE, 2023, 15 (01) : 160 - 180
[22] Researches on Intelligent Traffic Signal Control Based on Deep Reinforcement Learning
Luo, Juan
Li, Xinyu
Zheng, Yanliu
[J]. 2020 16TH INTERNATIONAL CONFERENCE ON MOBILITY, SENSING AND NETWORKING (MSN 2020), 2020, : 729 - 734
[23] An Intelligent Traffic Signal Control System Based on Deep Reinforcement Learning
Zhou, Chenming
Liu, Su
Li, Xinyu
Ruan, Junhui
Gao, Yayu
[J]. 2020 INFORMATION COMMUNICATION TECHNOLOGIES CONFERENCE (ICTC), 2020, : 255 - 259
[24] Model-Based Deep Reinforcement Learning with Traffic Inference for Traffic Signal Control
Wang, Hao
Zhu, Jinan
Gu, Bao
[J]. APPLIED SCIENCES-BASEL, 2023, 13 (06):
[25] Multi-agent deep reinforcement learning with traffic flow for traffic signal control
Hou, Liang
Huang, Dailin
Cao, Jie
Ma, Jialin
[J]. JOURNAL OF CONTROL AND DECISION, 2023,
[26] Optimizing Traffic Signal Control in Mixed Traffic Scenarios: A Predictive Traffic Information-based Deep Reinforcement Learning Approach
Zhang, Zhengyang
Zhou, Bin
Zhang, Bugao
Cheng, Ping
Lee, Der-Horng
Hu, Simon
[J]. 2024 FORUM FOR INNOVATIVE SUSTAINABLE TRANSPORTATION SYSTEMS, FISTS, 2024,
[27] A deep reinforcement learning-based cooperative approach formulti-intersection traffic signal control
Haddad, Tarek Amine
Hedjazi, Djalal
Aouag, Sofiane
[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2022, 114
[28] Deep Learning vs. Discrete Reinforcement Learning for Adaptive Traffic Signal Control
Shabestary, Soheil Mohamad Alizadeh
Abdulhai, Baher
[J]. 2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2018, : 286 - 293
[29] A Reinforcement Learning Approach for Intelligent Traffic Signal Control at Urban Intersections
Guo, Mengyu
Wang, Pin
Chan, Ching-Yao
Askary, Sid
[J]. 2019 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC), 2019, : 4242 - 4247
[30] A scalable approach to optimize traffic signal control with federated reinforcement learning
Bao, Jingjing
Wu, Celimuge
Lin, Yangfei
Zhong, Lei
Chen, Xianfu
Yin, Rui
[J]. SCIENTIFIC REPORTS, 2023, 13 (01)

← 1 2 3 4 5 →