An Experimental Review of Reinforcement Learning Algorithms for Adaptive Traffic Signal Control

James Duggan; Patrick Mannion; Enda Howley

doi:10.1007/978-3-319-25808-9_4

An Experimental Review of Reinforcement Learning Algorithms for Adaptive Traffic Signal Control

Research output: Chapter in Book or Conference Publication/Proceeding › Chapter › peer-review

Abstract

Urban traffic congestion has become a serious issue, and improving the flow of traffic through cities is critical for environmental, social and economic reasons. Improvements in Adaptive Traffic Signal Control (ATSC) have a pivotal role to play in the future development of Smart Cities and in the alleviation of traffic congestion. Here we describe an autonomic method for ATSC, namely, reinforcement learning (RL). This chapter presents a comprehensive review of the applications of RL to the traffic control problem to date, along with a case study that showcases our developing multi-agent traffic control architecture. Three different RL algorithms are presented and evaluated experimentally. We also look towards the future and discuss some important challenges that still need to be addressed in this field. pp 47-66

Original language	English (Ireland)
Title of host publication	Autonomic Road Transport Support Systems
Publisher	SPRINGER INTERNATIONAL PUBLISHING AG
ISBN (Electronic)	978-3-319-25808-9
ISBN (Print)	978-3-319-25808-9
DOIs	https://doi.org/10.1007/978-3-319-25808-9_4
Publication status	Published - 1 May 2016

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

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Authors
Mannion, P; Duggan, J; Howley, E

Access to Document

10.1007/978-3-319-25808-9_4

Cite this

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abstract = "Urban traffic congestion has become a serious issue, and improving the flow of traffic through cities is critical for environmental, social and economic reasons. Improvements in Adaptive Traffic Signal Control (ATSC) have a pivotal role to play in the future development of Smart Cities and in the alleviation of traffic congestion. Here we describe an autonomic method for ATSC, namely, reinforcement learning (RL). This chapter presents a comprehensive review of the applications of RL to the traffic control problem to date, along with a case study that showcases our developing multi-agent traffic control architecture. Three different RL algorithms are presented and evaluated experimentally. We also look towards the future and discuss some important challenges that still need to be addressed in this field. pp 47-66",

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An Experimental Review of Reinforcement Learning Algorithms for Adaptive Traffic Signal Control

Abstract

UN SDGs

Authors (Note for portal: view the doc link for the full list of authors)

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