Equivalences between analytical railway capacity methods

IF 2.6 Q3 TRANSPORTATION
Qinglun Zhong , Chang’an Xu , Rudong Yang , Qingwei Zhong
{"title":"Equivalences between analytical railway capacity methods","authors":"Qinglun Zhong ,&nbsp;Chang’an Xu ,&nbsp;Rudong Yang ,&nbsp;Qingwei Zhong","doi":"10.1016/j.jrtpm.2022.100367","DOIUrl":null,"url":null,"abstract":"<div><p>Capacity analysis is of central importance in railway operation. Existing methods divide the infrastructure of question into smaller sections when computing the consumed capacity, which makes them nontransferable for real-world operation. We first review and enhance the UIC compression method, which results in a combination–reconstruction (ComRec) method to compute the compressed timetable graph of the whole infrastructure. Secondly, we propose a triangular-gap-problem-based (TGP) method to compute the headway times of train pairs when no more than one train lies within the separation gap of two trains. Then we show TGP method produces an compressed timetable graph equivalent to that by the ComRec method. Max-plus algebra approach determines the consumed capacity by solving an eigenvalue problem, and the solution corresponds to a timed event network as the compressed timetable. And by their correspondence, we show that these three methods are equivalent. Finally, we establish correspondences between the capacity methods and linear programming models. In this way, we were able to specify the condition when they give the same result and how infrastructure dividing underestimates capacity.</p></div>","PeriodicalId":51821,"journal":{"name":"Journal of Rail Transport Planning & Management","volume":"25 ","pages":"Article 100367"},"PeriodicalIF":2.6000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Rail Transport Planning & Management","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210970622000671","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TRANSPORTATION","Score":null,"Total":0}
引用次数: 2

Abstract

Capacity analysis is of central importance in railway operation. Existing methods divide the infrastructure of question into smaller sections when computing the consumed capacity, which makes them nontransferable for real-world operation. We first review and enhance the UIC compression method, which results in a combination–reconstruction (ComRec) method to compute the compressed timetable graph of the whole infrastructure. Secondly, we propose a triangular-gap-problem-based (TGP) method to compute the headway times of train pairs when no more than one train lies within the separation gap of two trains. Then we show TGP method produces an compressed timetable graph equivalent to that by the ComRec method. Max-plus algebra approach determines the consumed capacity by solving an eigenvalue problem, and the solution corresponds to a timed event network as the compressed timetable. And by their correspondence, we show that these three methods are equivalent. Finally, we establish correspondences between the capacity methods and linear programming models. In this way, we were able to specify the condition when they give the same result and how infrastructure dividing underestimates capacity.

分析铁路运力方法的等价性
运力分析在铁路运营中具有重要意义。现有的方法在计算消耗的容量时将有问题的基础设施划分为更小的部分,这使得它们对于现实世界的操作是不可转移的。我们首先回顾并改进了UIC压缩方法,该方法产生了一种组合重建(ComRec)方法来计算整个基础设施的压缩时间表图。其次,我们提出了一种基于三角形间隙问题(TGP)的方法来计算当不超过一列列车位于两列列车的间隔间隙内时列车对的间隔时间。然后我们证明了TGP方法产生的压缩时间表图与ComRec方法产生的时间表图等价。最大加代数方法通过求解特征值问题来确定消耗的容量,并且该解决方案对应于作为压缩时间表的定时事件网络。通过它们的对应关系,我们证明了这三种方法是等价的。最后,我们建立了容量方法和线性规划模型之间的对应关系。通过这种方式,我们能够指定它们给出相同结果的条件,以及基础设施划分如何低估容量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
7.10
自引率
8.10%
发文量
41
×
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