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A learning technique for deploying self-tuning traffic control systems

Papageorgiou Markos, Kosmatopoulos Ilias, Ioannis Papamichail, Kouvelas Anastasios

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URIhttp://purl.tuc.gr/dl/dias/B36CF5D1-2B51-4BF0-8891-BBB79360CB31-
Identifierhttps://doi.org/10.1109/ITSC.2011.6082968-
Languageen-
TitleA learning technique for deploying self-tuning traffic control systemsen
CreatorPapageorgiou Markosen
CreatorΠαπαγεωργιου Μαρκοςel
CreatorKosmatopoulos Iliasen
CreatorΚοσματοπουλος Ηλιαςel
CreatorIoannis Papamichailen
CreatorKouvelas Anastasiosen
CreatorΚουβελας Αναστασιοςel
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryCurrently, a considerable amount of human effort and time is spent for initialization or calibration of operational traffic control systems. Typically, this optimization (fine-tuning) procedure is conducted manually, via trial-and-error, relying on expertise and human judgment and does not always lead to a desirable outcome. This paper presents a new learning/adaptive algorithm that enables automatic fine-tuning of general traffic control systems. The efficiency and online feasibility of the algorithm is investigated through extensive simulation experiments. The fine-tuning problem of three mutually-interacting control modules - each one with its distinct design parameters - of an urban traffic signal control strategy is thoroughly investigated. Simulation results indicate that the learning algorithm can provide efficient automatic fine-tuning, guaranteeing safe and convergent behavior.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-09-
Date of Publication2011-
Bibliographic CitationA. Kouvelas, M. Papageorgiou, E.B. Kosmatopoulos, I. Papamichail, "A learning technique for deploying self-tuning traffic control systems," in 14th International IEEE Conference on Intelligent Transportation Systems, 2011, pp. 1646-1651. doi: 10.1109/ITSC.2011.6082968en

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