Optimizing Urban Traffic Flow: From Traditional TSC to Multi-Agent Reinforcement Learning

Traffic congestion in emerging megacities is exacerbated by rapid population growth and urbanization. Traditional Traffic Signal Control (TSC) methods struggle with dynamic and unpredictable conditions, facing computational and storage challenges. This paper explores modern TSC methods using advance

Authors

Zejun Ding (ORCID: https://orcid.org/0000-0001-5767-1145)
Yunfan Zhang (ORCID: https://orcid.org/0000-0002-2342-1997)
Yanbin Hou

Publication Details

Published: 2025-01-01
DOI: https://doi.org/10.4108/eai.21-11-2024.2354610
Primary Topic: Traffic control and management
Type: article
Field-Weighted Citation Impact: 0.00

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article

Optimizing Urban Traffic Flow: From Traditional TSC to Multi-Agent Reinforcement Learning

Zejun Ding, Yunfan Zhang, Yanbin Hou

Jan 1, 2025

Traffic control and management

article

Optimizing Urban Traffic Flow: From Traditional TSC to Multi-Agent Reinforcement Learning

Zejun Ding, Yunfan Zhang, Yanbin Hou

January 1, 2025article en

Abstract

Sustainable cities and communities

Openalex Percentile: Top 95%

Traffic control and management

DOI Open Access PDF OpenAlex

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Optimizing Urban Traffic Flow: From Traditional TSC to Multi-Agent Reinforcement Learning — Zejun Ding, Yunfan Zhang, et al. · (2025) | TGRS Research Map | TGRS