Xinxin Yu, Changzhi Bian, Heling Liu, J. Shao, Xiaoxia Yao, Guoyi Tang, Xiongjun Han, Ying Liu
{"title":"Highway network design model with value-at-risk","authors":"Xinxin Yu, Changzhi Bian, Heling Liu, J. Shao, Xiaoxia Yao, Guoyi Tang, Xiongjun Han, Ying Liu","doi":"10.1117/12.2652300","DOIUrl":null,"url":null,"abstract":"In order to improve the traditional planning method, this paper establishes traffic network design model under uncertainty theory, so as to improve the rationality of the traffic network planning scheme. This paper assumes that the traffic demand is a random variable, and then establishes a bi-level model. The upper model takes the sum of the total travel time and VaR as the objective function, and the lower model uses the user equilibrium allocation model. The genetic algorithm with Monte Carlo simulation is used to solve the stochastic network optimization problem. The example analysis shows that: (1) The uncertainty of demand has a significant impact on the network construction scheme. (2) Network planning scheme will be affected by the risk attitude of the decision maker.","PeriodicalId":116712,"journal":{"name":"Frontiers of Traffic and Transportation Engineering","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers of Traffic and Transportation Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2652300","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
In order to improve the traditional planning method, this paper establishes traffic network design model under uncertainty theory, so as to improve the rationality of the traffic network planning scheme. This paper assumes that the traffic demand is a random variable, and then establishes a bi-level model. The upper model takes the sum of the total travel time and VaR as the objective function, and the lower model uses the user equilibrium allocation model. The genetic algorithm with Monte Carlo simulation is used to solve the stochastic network optimization problem. The example analysis shows that: (1) The uncertainty of demand has a significant impact on the network construction scheme. (2) Network planning scheme will be affected by the risk attitude of the decision maker.