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Developing adaptive traffic signal control by actor-critic and direct exploration methods
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Computer science. (Geospatial Informationsvetenskap)
Division of Visual Information and Interaction, Department of Information Technology, Uppsala University, Uppsala, Sweden.
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Computer science. Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, Iran.
Institute of Artificial Intelligence and Cognitive Engineering, University of Groningen, Groningen, Netherlands.
2018 (English)In: Proceedings of the Institution of Civil Engineers: Transport, ISSN 0965-092X, E-ISSN 1751-7710, Vol. 172, no 5, p. 289-298Article in journal (Refereed) Published
Abstract [en]

Designing efficient traffic signal controllers has always been an important concern in traffic engineering. This is owing to the complex and uncertain nature of traffic environments. Within such a context, reinforcement learning has been one of the most successful methods owing to its adaptability and its online learning ability. Reinforcement learning provides traffic signals with the ability automatically to determine the ideal behaviour for achieving their objective (alleviating traffic congestion). In fact, traffic signals based on reinforcement learning are able to learn and react flexibly to different traffic situations without the need of a predefined model of the environment. In this research, the actor-critic method is used for adaptive traffic signal control (ATSC-AC). Actor-critic has the advantages of both actor-only and critic-only methods. One of the most important issues in reinforcement learning is the trade-off between exploration of the traffic environment and exploitation of the knowledge already obtained. In order to tackle this challenge, two direct exploration methods are adapted to traffic signal control and compared with two indirect exploration methods. The results reveal that ATSC-ACs based on direct exploration methods have the best performance and they consistently outperform a fixed-time controller, reducing average travel time by 21%.

Place, publisher, year, edition, pages
Thomas Telford, 2018. Vol. 172, no 5, p. 289-298
Keywords [en]
communications & control systems; traffic engineering; transport management
National Category
Civil Engineering Computer and Information Sciences
Identifiers
URN: urn:nbn:se:hig:diva-28332DOI: 10.1680/jtran.17.00085OAI: oai:DiVA.org:hig-28332DiVA, id: diva2:1256155
Available from: 2018-10-16 Created: 2018-10-16 Last updated: 2019-10-11Bibliographically approved

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Aslani, MohammadSeipel, Stefan

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