{"title":"Traffic light control based on fuzzy Q-leaming","authors":"M. J. Moghaddam, Matin Hosseini, R. Safabakhsh","doi":"10.1109/AISP.2015.7123500","DOIUrl":null,"url":null,"abstract":"Traffic is an issue that many big cities are confronted with because of ever-increasing population growth. In this paper we propose a two phase traffic light control system based on fuzzy Q-learning for an isolated 4-way intersection. The states and actions of the Q-learning variables is set by a fuzzy algorithm which can be learned through environmental interactions and taking advantage of fuzzy logic. The proposed algorithm was simulated for a period of one hour for each of 14 different traffic conditions. Comparison with other methods was carried out on the 14 traffic conditions. The results showed that the proposed algorithms decrease the total waiting time and the mean of queue length.","PeriodicalId":405857,"journal":{"name":"2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AISP.2015.7123500","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18
Abstract
Traffic is an issue that many big cities are confronted with because of ever-increasing population growth. In this paper we propose a two phase traffic light control system based on fuzzy Q-learning for an isolated 4-way intersection. The states and actions of the Q-learning variables is set by a fuzzy algorithm which can be learned through environmental interactions and taking advantage of fuzzy logic. The proposed algorithm was simulated for a period of one hour for each of 14 different traffic conditions. Comparison with other methods was carried out on the 14 traffic conditions. The results showed that the proposed algorithms decrease the total waiting time and the mean of queue length.