具有风险价值的公路网设计模型

Xinxin Yu, Changzhi Bian, Heling Liu, J. Shao, Xiaoxia Yao, Guoyi Tang, Xiongjun Han, Ying Liu
{"title":"具有风险价值的公路网设计模型","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":"{\"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}","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

摘要

为了改进传统的规划方法,本文建立了不确定性理论下的交通网络设计模型,以提高交通网络规划方案的合理性。本文假设交通需求是一个随机变量,建立了一个双层模型。上层模型以总行程时间和VaR之和为目标函数,下层模型采用用户均衡分配模型。采用蒙特卡罗模拟遗传算法求解随机网络优化问题。算例分析表明:(1)需求的不确定性对电网建设方案有显著影响。(2)网络规划方案会受到决策者风险态度的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Highway network design model with value-at-risk
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信