Optimizing the train timetable in a high-speed rail corridor: The implications on departure time, fare cost and seat preference of passengers.

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 app...

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Main Authors: Zhipeng Huang, Limin Yang, Jinlian Li, Tao Zhang, Zixian Qu, Yusen Miao
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0326170
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author Zhipeng Huang
Limin Yang
Jinlian Li
Tao Zhang
Zixian Qu
Yusen Miao
author_facet Zhipeng Huang
Limin Yang
Jinlian Li
Tao Zhang
Zixian Qu
Yusen Miao
author_sort Zhipeng Huang
collection DOAJ
description 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.
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spelling doaj-art-bab0b2a45a6749ffaa9c45ec1ccfcd392025-08-20T03:47:13ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01206e032617010.1371/journal.pone.0326170Optimizing the train timetable in a high-speed rail corridor: The implications on departure time, fare cost and seat preference of passengers.Zhipeng HuangLimin YangJinlian LiTao ZhangZixian QuYusen MiaoHigh-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.https://doi.org/10.1371/journal.pone.0326170
spellingShingle Zhipeng Huang
Limin Yang
Jinlian Li
Tao Zhang
Zixian Qu
Yusen Miao
Optimizing the train timetable in a high-speed rail corridor: The implications on departure time, fare cost and seat preference of passengers.
PLoS ONE
title Optimizing the train timetable in a high-speed rail corridor: The implications on departure time, fare cost and seat preference of passengers.
title_full Optimizing the train timetable in a high-speed rail corridor: The implications on departure time, fare cost and seat preference of passengers.
title_fullStr Optimizing the train timetable in a high-speed rail corridor: The implications on departure time, fare cost and seat preference of passengers.
title_full_unstemmed Optimizing the train timetable in a high-speed rail corridor: The implications on departure time, fare cost and seat preference of passengers.
title_short Optimizing the train timetable in a high-speed rail corridor: The implications on departure time, fare cost and seat preference of passengers.
title_sort optimizing the train timetable in a high speed rail corridor the implications on departure time fare cost and seat preference of passengers
url https://doi.org/10.1371/journal.pone.0326170
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