{"title":"基于遗传算法的交通流波动自适应偏移优化","authors":"S. Takahashi, H. Nakamura, H. Kazama, T. Fujikura","doi":"10.1109/ITSC.2002.1041316","DOIUrl":null,"url":null,"abstract":"This paper describes offset optimization for the fluctuations of traffic flow using a genetic algorithm (GA). An offset, which is the target of signal control parameters for this study, is difficult to optimize because of its variety of combinations. Traffic signal optimization using GAs has has been investigated in previous studies, most of which focused on signal control without considering the fluctuations of traffic flow. In a practical situation, the rate of flow changes as time passes, so that offset optimization considering these fluctuations of flow is required. As a case study, an urban traffic route in a city of the Chubu region in Japan, with twenty-one signalized intersections, was tested. To perform offset-optimization by a GA, offset values were represented in a chromosome having the same number of genes as the signals. Two different schemes are introduced into the GA-based program and evaluated in terms of average travel time. The results show that the offset optimization schemes used in this study are valuable for efficient signal control.","PeriodicalId":365722,"journal":{"name":"Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Genetic algorithm approach for adaptive offset optimization for the fluctuation of traffic flow\",\"authors\":\"S. Takahashi, H. Nakamura, H. Kazama, T. Fujikura\",\"doi\":\"10.1109/ITSC.2002.1041316\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes offset optimization for the fluctuations of traffic flow using a genetic algorithm (GA). An offset, which is the target of signal control parameters for this study, is difficult to optimize because of its variety of combinations. Traffic signal optimization using GAs has has been investigated in previous studies, most of which focused on signal control without considering the fluctuations of traffic flow. In a practical situation, the rate of flow changes as time passes, so that offset optimization considering these fluctuations of flow is required. As a case study, an urban traffic route in a city of the Chubu region in Japan, with twenty-one signalized intersections, was tested. To perform offset-optimization by a GA, offset values were represented in a chromosome having the same number of genes as the signals. Two different schemes are introduced into the GA-based program and evaluated in terms of average travel time. The results show that the offset optimization schemes used in this study are valuable for efficient signal control.\",\"PeriodicalId\":365722,\"journal\":{\"name\":\"Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITSC.2002.1041316\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITSC.2002.1041316","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Genetic algorithm approach for adaptive offset optimization for the fluctuation of traffic flow
This paper describes offset optimization for the fluctuations of traffic flow using a genetic algorithm (GA). An offset, which is the target of signal control parameters for this study, is difficult to optimize because of its variety of combinations. Traffic signal optimization using GAs has has been investigated in previous studies, most of which focused on signal control without considering the fluctuations of traffic flow. In a practical situation, the rate of flow changes as time passes, so that offset optimization considering these fluctuations of flow is required. As a case study, an urban traffic route in a city of the Chubu region in Japan, with twenty-one signalized intersections, was tested. To perform offset-optimization by a GA, offset values were represented in a chromosome having the same number of genes as the signals. Two different schemes are introduced into the GA-based program and evaluated in terms of average travel time. The results show that the offset optimization schemes used in this study are valuable for efficient signal control.