{"title":"Improved Genetic Algorithm for the Regional Multi-line Bus Dispatching","authors":"Jinling Du, L. Cao","doi":"10.1109/CIS.2013.183","DOIUrl":null,"url":null,"abstract":"Bus trip is a healthy way to travel, and optimal bus dispatching decides social and economic benefits. Based on the analysis for both the existing traffic environment and bus dispatching, a mathematical model of the regional multi-line bus dispatching within is proposed in this paper such that the goal function of this model are the average maximal satisfaction for passengers, the average loading rate of the maximum and the minimal average bus departure frequency of the bus company, respectively. Furthermore, the genetic algorithm is improved further to prevent premature for the algorithm and ensure fast convergence of the algorithm. Finally, case analysis gives a satisfying departure interval for each time period in a day and verifies the effectiveness of the algorithm.","PeriodicalId":294223,"journal":{"name":"2013 Ninth International Conference on Computational Intelligence and Security","volume":"148 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Ninth International Conference on Computational Intelligence and Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIS.2013.183","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Bus trip is a healthy way to travel, and optimal bus dispatching decides social and economic benefits. Based on the analysis for both the existing traffic environment and bus dispatching, a mathematical model of the regional multi-line bus dispatching within is proposed in this paper such that the goal function of this model are the average maximal satisfaction for passengers, the average loading rate of the maximum and the minimal average bus departure frequency of the bus company, respectively. Furthermore, the genetic algorithm is improved further to prevent premature for the algorithm and ensure fast convergence of the algorithm. Finally, case analysis gives a satisfying departure interval for each time period in a day and verifies the effectiveness of the algorithm.