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Continuous residual reinforcement learning for traffic signal control optimization
Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Datavetenskap.
Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Datavetenskap. Division of Visual Information and Interaction, Department of Information Technology, Uppsala University, Uppsala, Sweden.
Institute of Artificial Intelligence and Cognitive Engineering, University of Groningen, Groningen, the Netherlands.
2018 (Engelska)Ingår i: Canadian journal of civil engineering (Print), ISSN 0315-1468, E-ISSN 1208-6029, Vol. 45, nr 8, s. 690-702Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Traffic signal control can be naturally regarded as a reinforcement learning problem. Unfortunately, it is one of the most difficult classes of reinforcement learning problems owing to its large state space. A straightforward approach to address this challenge is to control traffic signals based on continuous reinforcement learning. Although they have been successful in traffic signal control, they may become unstable and fail to converge to near-optimal solutions. We develop adaptive traffic signal controllers based on continuous residual reinforcement learning (CRL-TSC) that is more stable. The effect of three feature functions is empirically investigated in a microscopic traffic simulation. Furthermore, the effects of departing streets, more actions, and the use of the spatial distribution of the vehicles on the performance of CRL-TSCs are assessed. The results show that the best setup of the CRL-TSC leads to saving average travel time by 15% in comparison to an optimized fixed-time controller.

Ort, förlag, år, upplaga, sidor
NRC Research Press , 2018. Vol. 45, nr 8, s. 690-702
Nyckelord [en]
continuous state reinforcement learning, adaptive traffic signal control, microscopic traffic simulation
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Identifikatorer
URN: urn:nbn:se:hig:diva-27845DOI: 10.1139/cjce-2017-0408ISI: 000440632100009Scopus ID: 2-s2.0-85051122432OAI: oai:DiVA.org:hig-27845DiVA, id: diva2:1245454
Tillgänglig från: 2018-09-05 Skapad: 2018-09-05 Senast uppdaterad: 2018-09-05Bibliografiskt granskad

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Aslani, MohammadSeipel, Stefan

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