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A decentralized optimization approach employing cooperative cycle-regulation in an intersection-centric manner: a complex urban simulative case study

Michailidis Iakovos T., Manolis Diamantis, Michailidis Panagiotis, Diakaki Christina, Kosmatopoulos Ilias

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URIhttp://purl.tuc.gr/dl/dias/DB824EDC-27F7-4F05-9951-BAD815AC0FEF-
Identifierhttps://doi.org/10.1016/j.trip.2020.100232-
Identifierhttps://www.sciencedirect.com/science/article/pii/S2590198220301433-
Languageen-
Extent12 pagesen
Extent2,13 megabytesen
TitleA decentralized optimization approach employing cooperative cycle-regulation in an intersection-centric manner: a complex urban simulative case study en
CreatorMichailidis Iakovos T.en
CreatorManolis Diamantisen
CreatorΜανωλης Διαμαντηςel
CreatorMichailidis Panagiotisen
CreatorDiakaki Christinaen
CreatorΔιακακη Χριστιναel
CreatorKosmatopoulos Iliasen
CreatorΚοσματοπουλος Ηλιαςel
PublisherElsevieren
Content SummaryThe upcoming high population density rise in metropolitan areas is anticipated to further deteriorate the traffic conditions. To tackle this problem, advanced ICT applications have been employed, able to monitor and manage traffic in real time. In practice, to efficiently correspond to dynamic traffic conditions those applications require to be frequently reconfigured – an operation that usually involves expert-teams manually adjusting the traffic-regulating strategies regularly. However, these manual procedures are not adequately aligned with the traffic situation since complicated stochastic dynamics, model unavailability and data inner-transmission constraints usually emerge. In order to overcome such cumbersome and expensive adjustment procedures modern decentralized adaptive optimization is widely accepted and recognized as an efficient automated solution for tuning the control strategy on-the-fly. Motivated by the above, L4GCAO, a decentralized, model independent, flexible optimization technique has been designed for optimizing cycle management at a local level to improve network performance at the global level, by automatically adjusting the cycle-regulating parameters in an intersection-centric manner, through cooperating self-learning agents. This paper studies L4GCAO's first application on a realistic traffic-network simulation scheme that examines the online fine-tuning process of the cycle-regulating parameters. Moreover, in order to evaluate the decentralized L4GCAO performance, two levels of performance benchmarking have been considered: a comparison with CAO - its well-established centralized counterpart; an already well-designed fixed-time management plan. In all cases, L4GCAO exhibits an almost equivalent performance improvement compared to CAO, both with respect to a properly fixed-time traffic management plan, while utilizes less parameters in a non-centralized manner.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/en
Date of Item2021-12-28-
Date of Publication2020-
SubjectUrban traffic networken
SubjectDistributed cycle regulationen
SubjectIntersection-centric cycle regulationen
SubjectDistributed fine-tuningen
SubjectOverall network-productivity indexen
Bibliographic CitationI. T. Michailidis, D. Manolis, P. Michailidis, C. Diakaki, and E. B. Kosmatopoulos, “A decentralized optimization approach employing cooperative cycle-regulation in an intersection-centric manner: a complex urban simulative case study,” Transp. Res. Interdiscip. Perspect., vol. 8, Nov. 2020. doi: 10.1016/j.trip.2020.100232en

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