Jianguo Qi , Yaru Zhou , Fanting Meng , Lixing Yang , Qin Luo , Chuntian Zhang
{"title":"Joint optimization of train stop planning and timetabling with time-dependent passenger and freight demands in high-speed railway","authors":"Jianguo Qi , Yaru Zhou , Fanting Meng , Lixing Yang , Qin Luo , Chuntian Zhang","doi":"10.1016/j.trc.2025.105025","DOIUrl":null,"url":null,"abstract":"<div><div>To effectively utilize the remaining transportation capacity and increase the revenue of railway transport enterprises during periods of lower passenger demand, this study proposes a general framework for the joint optimization of train stop planning and timetabling problems based on a flexible train composition mode for passenger and freight co-transportation on high-speed railways. This framework aims to simultaneously provide different transport services for time-dependent passenger and freight demands. With the basic services being satisfied, a quadratic programming model is formulated to maximize the transportation revenue of railway companies by considering different operation costs of different types of train composition modes and minimizing the time deviations of passenger and freight compared with their desired service times. To effectively solve the proposed model, a heuristic approach combining the variable neighborhood search (VNS) and the commercial solver CPLEX is designed to search for high-quality solutions. Finally, the performance and effectiveness of the proposed approaches are verified using a small-scale example and a real case study of the Wuhan-Guangzhou high-speed railway.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"172 ","pages":"Article 105025"},"PeriodicalIF":7.6000,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part C-Emerging Technologies","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0968090X25000294","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TRANSPORTATION SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
To effectively utilize the remaining transportation capacity and increase the revenue of railway transport enterprises during periods of lower passenger demand, this study proposes a general framework for the joint optimization of train stop planning and timetabling problems based on a flexible train composition mode for passenger and freight co-transportation on high-speed railways. This framework aims to simultaneously provide different transport services for time-dependent passenger and freight demands. With the basic services being satisfied, a quadratic programming model is formulated to maximize the transportation revenue of railway companies by considering different operation costs of different types of train composition modes and minimizing the time deviations of passenger and freight compared with their desired service times. To effectively solve the proposed model, a heuristic approach combining the variable neighborhood search (VNS) and the commercial solver CPLEX is designed to search for high-quality solutions. Finally, the performance and effectiveness of the proposed approaches are verified using a small-scale example and a real case study of the Wuhan-Guangzhou high-speed railway.
期刊介绍:
Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.