Pietro Grandinetti, Federica Garin, C. Canudas-de-Wit
{"title":"Towards scalable optimal traffic control","authors":"Pietro Grandinetti, Federica Garin, C. Canudas-de-Wit","doi":"10.1109/CDC.2015.7402529","DOIUrl":null,"url":null,"abstract":"This paper deals with scalable control of traffic lights in urban traffic networks. Optimization is done in real time, so as to take into account variable traffic demands. At each cycle of the traffic lights, the optimization concerns time instants where each traffic light starts and ends its green phase: this allows to describe both the duty-cycle and the phase shifts. First, we formulate a global optimization problem, which can be cast as a mixed-integer linear program. To overcome the complexity of this centralized approach, we also propose a decentralized suboptimal algorithm, whose simplicity allows online implementation. Simulations show the effectiveness of the proposed strategies.","PeriodicalId":308101,"journal":{"name":"2015 54th IEEE Conference on Decision and Control (CDC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 54th IEEE Conference on Decision and Control (CDC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC.2015.7402529","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
This paper deals with scalable control of traffic lights in urban traffic networks. Optimization is done in real time, so as to take into account variable traffic demands. At each cycle of the traffic lights, the optimization concerns time instants where each traffic light starts and ends its green phase: this allows to describe both the duty-cycle and the phase shifts. First, we formulate a global optimization problem, which can be cast as a mixed-integer linear program. To overcome the complexity of this centralized approach, we also propose a decentralized suboptimal algorithm, whose simplicity allows online implementation. Simulations show the effectiveness of the proposed strategies.