Fuzzy logic and deep Q learning based control for traffic lights

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dc.authorid0000-0003-2239-0954
dc.contributor.authorTunc, Ilhan
dc.contributor.authorSoylemez, Mehmet Turan
dc.date.accessioned2026-02-12T21:05:33Z
dc.date.available2026-02-12T21:05:33Z
dc.date.issued2023
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractTraffic congestion is a major concern for many metropolises. Although it is difficult to regulate traffic flow because of numerous complexities and uncertainties, the traffic congestion problem must be mitigated in order to reduce the environmental problems related to traffic and the time lost on the roads in big cities. Intelligent traffic control methods, the use of which is increas-ing with the development of new methods, as opposed to conventional methods, and provide more efficient solutions, especially in traffic intersections with high traffic density. In this paper, we pro-pose a new agent-based Fuzzy Logic assisted traffic light signal timing for traffic intersections. Deep Q-Learning algorithms and Fuzzy Logic Control (FLC) are used together in the proposed method. In this study, the proposed method and many traffic light control methods in the literature were simulated. In order to demonstrate the effectiveness of the proposed method, some of the important metrics of evaluation such as traffic congestion, air pollution, and waiting time were used in the assessment of the simulation results. In addition, with the proposed method, it has been shown that the stability and robustness of the system are increased. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
dc.identifier.doi10.1016/j.aej.2022.12.028
dc.identifier.endpage359
dc.identifier.issn1110-0168
dc.identifier.issn2090-2670
dc.identifier.scopus2-s2.0-85145255477
dc.identifier.scopusqualityQ1
dc.identifier.startpage343
dc.identifier.urihttps://doi.org/10.1016/j.aej.2022.12.028
dc.identifier.urihttps://hdl.handle.net/20.500.12885/7025
dc.identifier.volume67
dc.identifier.wosWOS:000918219100001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofAlexandria Engineering Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260212
dc.subjectDeep Q learning
dc.subjectFuzzy logic control
dc.subjectTraffic light control
dc.titleFuzzy logic and deep Q learning based control for traffic lights
dc.typeArticle

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