Zhiya Su , Enoch Lee , Kejun Du , Qiru Ma , Hong K. Lo
{"title":"考虑不确定地铁系统中断的多式联运网络弹性公交服务设计","authors":"Zhiya Su , Enoch Lee , Kejun Du , Qiru Ma , Hong K. Lo","doi":"10.1016/j.trc.2025.105118","DOIUrl":null,"url":null,"abstract":"<div><div>Disruptions in the metro system often lead to chaos in the public transportation system due to their significant mode share. To mitigate such impacts, this study designs a multimodal public transportation network integrating metro and bus, subject to stochastic metro disruptions. With a given metro system, a two-stage stochastic programming model is formulated to design complementary bus services, catering to stochastically degradable metro capacity. Under normal metro operations, the bus services complement the metro services, but with built-in resiliency to handle potential disruptions. In the event of metro disruptions, they function as substitutes to mitigate the disruptive impact on passengers, thereby maintaining system reliability. The bus routings and service frequencies are designed to achieve social optimal by minimizing the combined costs of bus construction, operating expenses, expected total passenger costs, and unmet demand costs arising from metro disruptions. A service reliability-based solution method is adopted to solve the problem by decomposing the problem into two phases. In phase 1, given a service reliability measure, the model determines the bus routing and frequencies. Then, in phase 2, given the bus routes and frequencies, it minimizes the costs of lost demand and passenger inconvenience. A service overlapping penalty is considered to prevent substantial duplication between metro and bus services. The effectiveness of the proposed model is validated in a case study, demonstrating the advantages of considering stochastic degradable capacity and designing complementary bus services in an integrated multimodal public transportation system. Under various disruption conditions, the demand loss is reduced by over 95% compared tobenchmark cases.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105118"},"PeriodicalIF":7.6000,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Resilient bus services design in a multimodal network with uncertain metro system disruption\",\"authors\":\"Zhiya Su , Enoch Lee , Kejun Du , Qiru Ma , Hong K. Lo\",\"doi\":\"10.1016/j.trc.2025.105118\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Disruptions in the metro system often lead to chaos in the public transportation system due to their significant mode share. To mitigate such impacts, this study designs a multimodal public transportation network integrating metro and bus, subject to stochastic metro disruptions. With a given metro system, a two-stage stochastic programming model is formulated to design complementary bus services, catering to stochastically degradable metro capacity. Under normal metro operations, the bus services complement the metro services, but with built-in resiliency to handle potential disruptions. In the event of metro disruptions, they function as substitutes to mitigate the disruptive impact on passengers, thereby maintaining system reliability. The bus routings and service frequencies are designed to achieve social optimal by minimizing the combined costs of bus construction, operating expenses, expected total passenger costs, and unmet demand costs arising from metro disruptions. A service reliability-based solution method is adopted to solve the problem by decomposing the problem into two phases. In phase 1, given a service reliability measure, the model determines the bus routing and frequencies. Then, in phase 2, given the bus routes and frequencies, it minimizes the costs of lost demand and passenger inconvenience. A service overlapping penalty is considered to prevent substantial duplication between metro and bus services. The effectiveness of the proposed model is validated in a case study, demonstrating the advantages of considering stochastic degradable capacity and designing complementary bus services in an integrated multimodal public transportation system. Under various disruption conditions, the demand loss is reduced by over 95% compared tobenchmark cases.</div></div>\",\"PeriodicalId\":54417,\"journal\":{\"name\":\"Transportation Research Part C-Emerging Technologies\",\"volume\":\"174 \",\"pages\":\"Article 105118\"},\"PeriodicalIF\":7.6000,\"publicationDate\":\"2025-04-10\",\"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/S0968090X25001226\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part C-Emerging Technologies","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0968090X25001226","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}
Resilient bus services design in a multimodal network with uncertain metro system disruption
Disruptions in the metro system often lead to chaos in the public transportation system due to their significant mode share. To mitigate such impacts, this study designs a multimodal public transportation network integrating metro and bus, subject to stochastic metro disruptions. With a given metro system, a two-stage stochastic programming model is formulated to design complementary bus services, catering to stochastically degradable metro capacity. Under normal metro operations, the bus services complement the metro services, but with built-in resiliency to handle potential disruptions. In the event of metro disruptions, they function as substitutes to mitigate the disruptive impact on passengers, thereby maintaining system reliability. The bus routings and service frequencies are designed to achieve social optimal by minimizing the combined costs of bus construction, operating expenses, expected total passenger costs, and unmet demand costs arising from metro disruptions. A service reliability-based solution method is adopted to solve the problem by decomposing the problem into two phases. In phase 1, given a service reliability measure, the model determines the bus routing and frequencies. Then, in phase 2, given the bus routes and frequencies, it minimizes the costs of lost demand and passenger inconvenience. A service overlapping penalty is considered to prevent substantial duplication between metro and bus services. The effectiveness of the proposed model is validated in a case study, demonstrating the advantages of considering stochastic degradable capacity and designing complementary bus services in an integrated multimodal public transportation system. Under various disruption conditions, the demand loss is reduced by over 95% compared tobenchmark cases.
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.