Location-Based Real-Time Updated Advising Method for Traffic Signal Control

Zhu, C; Ye, D; Zhu, T; Zhou, W

Location-Based Real-Time Updated Advising Method for Traffic Signal Control

Zhu, C Ye, D

Zhu, T

Zhou, W

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2023, PP, (99), pp. 1-1
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Zhu, C
dc.contributor.author	Ye, D https://orcid.org/0000-0002-7561-0992
dc.contributor.author	Zhu, T https://orcid.org/0000-0003-3411-7947
dc.contributor.author	Zhou, W
dc.date.accessioned	2024-03-27T05:16:59Z
dc.date.available	2024-03-27T05:16:59Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Internet of Things Journal, 2023, PP, (99), pp. 1-1
dc.identifier.issn	2372-2541
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/177268
dc.description.abstract	Adaptive traffic signal control (ATSC) attempts to alleviate traffic congestion by dynamically adjusting the timing of traffic lights in real time, and multi-agent reinforcement learning is one of the ways these systems learn how and when to change signals. However, traffic congestion continues to be a problem in most highly-populated cities. We know that the current research into ATSC still has much ground to cover in terms of traffic efficiency, global optimality and convergence stability. Hence, in this paper, we outline a method that provides a advising method to the multi-agent traffic signal control based on relative location in real time. ATSC is regarded as a multi-agent environment, in which each traffic intersection is an agent to observe the distribution of the number of vehicles (state) at the intersection to control the change of signal lights (action). In our learning framework, each agent can not only take action by its advantage actor-critic model, but can also ask its neighboring agent for advice when it is not confident in its decision. The advice is generated by a real-time updated advising model, which is based on the state and relative location of neighboring agents. Because the advising model provides real-time feedback, we find that learning is more effective and convergence is more stable. Moreover, drawing on neighboring states during taking action avoids falling into a local optimality caused by only observing local states. Comparisons with similar methods show that our method brings a significant improvement in a range of evaluation criteria, such as queue lengths, vehicle speeds, and trip delays.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/LP220200808
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2023.3342480
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Location-Based Real-Time Updated Advising Method for Traffic Signal Control
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/Strength - CCSP - Centre for Cyber Security and Privacy
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-27T05:16:57Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Adaptive traffic signal control (ATSC) attempts to alleviate traffic congestion by dynamically adjusting the timing of traffic lights in real time, and multi-agent reinforcement learning is one of the ways these systems learn how and when to change signals. However, traffic congestion continues to be a problem in most highly-populated cities. We know that the current research into ATSC still has much ground to cover in terms of traffic efficiency, global optimality and convergence stability. Hence, in this paper, we outline a method that provides a advising method to the multi-agent traffic signal control based on relative location in real time. ATSC is regarded as a multi-agent environment, in which each traffic intersection is an agent to observe the distribution of the number of vehicles (state) at the intersection to control the change of signal lights (action). In our learning framework, each agent can not only take action by its advantage actor-critic model, but can also ask its neighboring agent for advice when it is not confident in its decision. The advice is generated by a real-time updated advising model, which is based on the state and relative location of neighboring agents. Because the advising model provides real-time feedback, we find that learning is more effective and convergence is more stable. Moreover, drawing on neighboring states during taking action avoids falling into a local optimality caused by only observing local states. Comparisons with similar methods show that our method brings a significant improvement in a range of evaluation criteria, such as queue lengths, vehicle speeds, and trip delays.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/177268