{"title":"Multi-intersections traffic signal intelligent control using collaborative q-learning algorithm","authors":"Chungui Li, Xianglei Yan, Fei-Ying Lin, Hongling Zhang","doi":"10.1109/ICNC.2011.6022063","DOIUrl":null,"url":null,"abstract":"Since congestion of traffic is ubiquitous in the modern city, optimizing the behavior of traffic lights for efficient traffic flow is a critically important goal. However,agents often select only locally optimal actions without coordinating their neighbor intersections. In this paper, an urban road traffic area-wide coordination control algorithm based on collaborative Q-learning is proposed. The agent model of traffic intersections is demonstrated. The algorithm substantially reduces average vehicular delay by using a collaborative Q-learning algorithm and can cooperative control of multiple intersections to achieve a near optimal control policy. The computer simulation results show that the control algorithm can effectively reduce the average delay time and play a very good control effect with multi-intersections, so the coordination method used in this paper is effective.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Seventh International Conference on Natural Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2011.6022063","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
Since congestion of traffic is ubiquitous in the modern city, optimizing the behavior of traffic lights for efficient traffic flow is a critically important goal. However,agents often select only locally optimal actions without coordinating their neighbor intersections. In this paper, an urban road traffic area-wide coordination control algorithm based on collaborative Q-learning is proposed. The agent model of traffic intersections is demonstrated. The algorithm substantially reduces average vehicular delay by using a collaborative Q-learning algorithm and can cooperative control of multiple intersections to achieve a near optimal control policy. The computer simulation results show that the control algorithm can effectively reduce the average delay time and play a very good control effect with multi-intersections, so the coordination method used in this paper is effective.