An HSR corridor with m stations and n trains.
<div><p>High-speed railway timetables are typically based on origin-destination (OD) passenger demand, establishing departure times and intervals for trains. Utilizing this data, operators systematically develop daily train timetables that are consistent across a defined operational cycl...
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| منشور في: |
2025
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| _version_ | 1852019212367691776 |
|---|---|
| author | Zhipeng Huang (1759759) |
| author2 | Limin Yang (391341) Jinlian Li (343111) Tao Zhang (43681) Zixian Qu (21568078) Yusen Miao (21568081) |
| author2_role | author author author author author |
| author_facet | Zhipeng Huang (1759759) Limin Yang (391341) Jinlian Li (343111) Tao Zhang (43681) Zixian Qu (21568078) Yusen Miao (21568081) |
| author_role | author |
| dc.creator.none.fl_str_mv | Zhipeng Huang (1759759) Limin Yang (391341) Jinlian Li (343111) Tao Zhang (43681) Zixian Qu (21568078) Yusen Miao (21568081) |
| dc.date.none.fl_str_mv | 2025-06-18T17:49:59Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0326170.g001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/An_HSR_corridor_with_m_stations_and_n_trains_/29359525 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Sociology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified xi &# 8217 speed railway timetables speed railway scheduling speed railway corridor speed rail operators speed rail corridor others remain difficult low occupancy rates genetic algorithm combined defined operational cycle various network arcs three key attributes resulting timetable balances xlink "> high level programming model establishing departure times analyzing passenger preferences uniform departure intervals passengers </ p departure times train timetable state three passenger demand integrates preferences dimensional network departure time wolfe method typically based travel demand seat preference seat classes scientific rigor nested frank impedance functions fare structures fare cost diverse demands consistent across case study approach enhances |
| dc.title.none.fl_str_mv | An HSR corridor with m stations and n trains. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>High-speed railway timetables are typically based on origin-destination (OD) passenger demand, establishing departure times and intervals for trains. Utilizing this data, operators systematically develop daily train timetables that are consistent across a defined operational cycle. However, this approach often overlooks individual passenger preferences for departure times, fares, and seat classes, leading to low occupancy rates for some trains while others remain difficult to book. In this article, with the number of trains predetermined and considering the diverse demands of passengers, we addresses these challenges by analyzing passenger preferences and optimizing train stopping patterns and adjacent train departure intervals. We propose a time-space-state three-dimensional network (TSSN) that integrates preferences for travel time, fares, and seat classes. Impedance functions for various network arcs are developed, incorporating these three key attributes of travel demand and transforming the passenger travel choice issue into a path selection problem within the TSSN. A bi-level programming model is formulated: the upper level optimizes train operations and fare structures, while the lower level employs user equilibrium (UE) theory to distribute OD passenger demands across trains. Using the Lanzhou-Xi’an high-speed railway corridor as a case study, we apply a genetic algorithm combined with a nested Frank-Wolfe method to solve the model. The resulting timetable balances the interests of high-speed rail operators and passengers, incorporating non-uniform departure intervals to better meet diverse travel needs. Ultimately, this approach enhances the scientific rigor and practicality of high-speed railway scheduling while accommodating passenger preferences effectively.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_47eddc80d8a4b2dd34d47db4b3bfe482 |
| identifier_str_mv | 10.1371/journal.pone.0326170.g001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29359525 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | An HSR corridor with m stations and n trains.Zhipeng Huang (1759759)Limin Yang (391341)Jinlian Li (343111)Tao Zhang (43681)Zixian Qu (21568078)Yusen Miao (21568081)SociologyScience PolicyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedxi &# 8217speed railway timetablesspeed railway schedulingspeed railway corridorspeed rail operatorsspeed rail corridorothers remain difficultlow occupancy ratesgenetic algorithm combineddefined operational cyclevarious network arcsthree key attributesresulting timetable balancesxlink "> highlevel programming modelestablishing departure timesanalyzing passenger preferencesuniform departure intervalspassengers </ pdeparture timestrain timetablestate threepassenger demandintegrates preferencesdimensional networkdeparture timewolfe methodtypically basedtravel demandseat preferenceseat classesscientific rigornested frankimpedance functionsfare structuresfare costdiverse demandsconsistent acrosscase studyapproach enhances<div><p>High-speed railway timetables are typically based on origin-destination (OD) passenger demand, establishing departure times and intervals for trains. Utilizing this data, operators systematically develop daily train timetables that are consistent across a defined operational cycle. However, this approach often overlooks individual passenger preferences for departure times, fares, and seat classes, leading to low occupancy rates for some trains while others remain difficult to book. In this article, with the number of trains predetermined and considering the diverse demands of passengers, we addresses these challenges by analyzing passenger preferences and optimizing train stopping patterns and adjacent train departure intervals. We propose a time-space-state three-dimensional network (TSSN) that integrates preferences for travel time, fares, and seat classes. Impedance functions for various network arcs are developed, incorporating these three key attributes of travel demand and transforming the passenger travel choice issue into a path selection problem within the TSSN. A bi-level programming model is formulated: the upper level optimizes train operations and fare structures, while the lower level employs user equilibrium (UE) theory to distribute OD passenger demands across trains. Using the Lanzhou-Xi’an high-speed railway corridor as a case study, we apply a genetic algorithm combined with a nested Frank-Wolfe method to solve the model. The resulting timetable balances the interests of high-speed rail operators and passengers, incorporating non-uniform departure intervals to better meet diverse travel needs. Ultimately, this approach enhances the scientific rigor and practicality of high-speed railway scheduling while accommodating passenger preferences effectively.</p></div>2025-06-18T17:49:59ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0326170.g001https://figshare.com/articles/figure/An_HSR_corridor_with_m_stations_and_n_trains_/29359525CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/293595252025-06-18T17:49:59Z |
| spellingShingle | An HSR corridor with m stations and n trains. Zhipeng Huang (1759759) Sociology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified xi &# 8217 speed railway timetables speed railway scheduling speed railway corridor speed rail operators speed rail corridor others remain difficult low occupancy rates genetic algorithm combined defined operational cycle various network arcs three key attributes resulting timetable balances xlink "> high level programming model establishing departure times analyzing passenger preferences uniform departure intervals passengers </ p departure times train timetable state three passenger demand integrates preferences dimensional network departure time wolfe method typically based travel demand seat preference seat classes scientific rigor nested frank impedance functions fare structures fare cost diverse demands consistent across case study approach enhances |
| status_str | publishedVersion |
| title | An HSR corridor with m stations and n trains. |
| title_full | An HSR corridor with m stations and n trains. |
| title_fullStr | An HSR corridor with m stations and n trains. |
| title_full_unstemmed | An HSR corridor with m stations and n trains. |
| title_short | An HSR corridor with m stations and n trains. |
| title_sort | An HSR corridor with m stations and n trains. |
| topic | Sociology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified xi &# 8217 speed railway timetables speed railway scheduling speed railway corridor speed rail operators speed rail corridor others remain difficult low occupancy rates genetic algorithm combined defined operational cycle various network arcs three key attributes resulting timetable balances xlink "> high level programming model establishing departure times analyzing passenger preferences uniform departure intervals passengers </ p departure times train timetable state three passenger demand integrates preferences dimensional network departure time wolfe method typically based travel demand seat preference seat classes scientific rigor nested frank impedance functions fare structures fare cost diverse demands consistent across case study approach enhances |