Xin Wu , Xinyu Wang , Taehooie Kim , Khaled Saleh , Huiling Fu , Chenfeng Xiong
{"title":"铁路乘客诱导、分流和事后需求的同时估计:基于约束计算图的可解释机器学习框架","authors":"Xin Wu , Xinyu Wang , Taehooie Kim , Khaled Saleh , Huiling Fu , Chenfeng Xiong","doi":"10.1016/j.tre.2025.104283","DOIUrl":null,"url":null,"abstract":"<div><div>Passenger flow on train lines is driven by how travelers respond to service offerings and constraints within the railway system, shaped primarily by three factors: <strong>Diverted demand</strong> refers to a shift in travelers’ choices toward different train lines, quantified by analyzing changes in the probability of selecting a particular train line within a given line plan. <strong>Induced demand</strong> arises when improvements in service quality led to an increase in passenger demand within a railway system. <strong>Ex-post demand</strong> occurs when seat capacity constraints force travelers to make choices that deviate from their initial preferences. This paper aims to develop a systematic and theoretically consistent methodology to estimate the three types of demand that drive overall demand variation. To integrate these estimation modules, a computational graph-based learning architecture is proposed to solve the railway passenger demand estimation (RPDE) problem. The RPDE problem simultaneously estimates passenger boarding and alighting at stations, OD trips between stations, and passenger flows loaded onto train lines. The behavioral parameters associated with travel time, ticket price, and line frequency are also calibrated. A novel four-stage adapted alternating direction method of multipliers (ADMM), enhanced by backpropagation, is proposed to solve the RPDE problem to ensure consistency between modules and enable efficient solutions. We demonstrate the effectiveness of the method through scenario analyses, quantifying the composition of the demand, and revealing their implications for policymaking. A real-world case study in the Beijing-Shanghai high-speed rail corridor is used to demonstrate the applicability of the proposed approach.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"202 ","pages":"Article 104283"},"PeriodicalIF":8.3000,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Simultaneous estimation of induced, diverted, and ex-post demand for railway passengers: an interpretable machine learning framework based on constrained computational graphs\",\"authors\":\"Xin Wu , Xinyu Wang , Taehooie Kim , Khaled Saleh , Huiling Fu , Chenfeng Xiong\",\"doi\":\"10.1016/j.tre.2025.104283\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Passenger flow on train lines is driven by how travelers respond to service offerings and constraints within the railway system, shaped primarily by three factors: <strong>Diverted demand</strong> refers to a shift in travelers’ choices toward different train lines, quantified by analyzing changes in the probability of selecting a particular train line within a given line plan. <strong>Induced demand</strong> arises when improvements in service quality led to an increase in passenger demand within a railway system. <strong>Ex-post demand</strong> occurs when seat capacity constraints force travelers to make choices that deviate from their initial preferences. This paper aims to develop a systematic and theoretically consistent methodology to estimate the three types of demand that drive overall demand variation. To integrate these estimation modules, a computational graph-based learning architecture is proposed to solve the railway passenger demand estimation (RPDE) problem. The RPDE problem simultaneously estimates passenger boarding and alighting at stations, OD trips between stations, and passenger flows loaded onto train lines. The behavioral parameters associated with travel time, ticket price, and line frequency are also calibrated. A novel four-stage adapted alternating direction method of multipliers (ADMM), enhanced by backpropagation, is proposed to solve the RPDE problem to ensure consistency between modules and enable efficient solutions. We demonstrate the effectiveness of the method through scenario analyses, quantifying the composition of the demand, and revealing their implications for policymaking. A real-world case study in the Beijing-Shanghai high-speed rail corridor is used to demonstrate the applicability of the proposed approach.</div></div>\",\"PeriodicalId\":49418,\"journal\":{\"name\":\"Transportation Research Part E-Logistics and Transportation Review\",\"volume\":\"202 \",\"pages\":\"Article 104283\"},\"PeriodicalIF\":8.3000,\"publicationDate\":\"2025-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Research Part E-Logistics and Transportation Review\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1366554525003242\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part E-Logistics and Transportation Review","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1366554525003242","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
Simultaneous estimation of induced, diverted, and ex-post demand for railway passengers: an interpretable machine learning framework based on constrained computational graphs
Passenger flow on train lines is driven by how travelers respond to service offerings and constraints within the railway system, shaped primarily by three factors: Diverted demand refers to a shift in travelers’ choices toward different train lines, quantified by analyzing changes in the probability of selecting a particular train line within a given line plan. Induced demand arises when improvements in service quality led to an increase in passenger demand within a railway system. Ex-post demand occurs when seat capacity constraints force travelers to make choices that deviate from their initial preferences. This paper aims to develop a systematic and theoretically consistent methodology to estimate the three types of demand that drive overall demand variation. To integrate these estimation modules, a computational graph-based learning architecture is proposed to solve the railway passenger demand estimation (RPDE) problem. The RPDE problem simultaneously estimates passenger boarding and alighting at stations, OD trips between stations, and passenger flows loaded onto train lines. The behavioral parameters associated with travel time, ticket price, and line frequency are also calibrated. A novel four-stage adapted alternating direction method of multipliers (ADMM), enhanced by backpropagation, is proposed to solve the RPDE problem to ensure consistency between modules and enable efficient solutions. We demonstrate the effectiveness of the method through scenario analyses, quantifying the composition of the demand, and revealing their implications for policymaking. A real-world case study in the Beijing-Shanghai high-speed rail corridor is used to demonstrate the applicability of the proposed approach.
期刊介绍:
Transportation Research Part E: Logistics and Transportation Review is a reputable journal that publishes high-quality articles covering a wide range of topics in the field of logistics and transportation research. The journal welcomes submissions on various subjects, including transport economics, transport infrastructure and investment appraisal, evaluation of public policies related to transportation, empirical and analytical studies of logistics management practices and performance, logistics and operations models, and logistics and supply chain management.
Part E aims to provide informative and well-researched articles that contribute to the understanding and advancement of the field. The content of the journal is complementary to other prestigious journals in transportation research, such as Transportation Research Part A: Policy and Practice, Part B: Methodological, Part C: Emerging Technologies, Part D: Transport and Environment, and Part F: Traffic Psychology and Behaviour. Together, these journals form a comprehensive and cohesive reference for current research in transportation science.