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Collaborative optimization for train stop planning and train timetabling on high-speed railways based on passenger demand
In recent years, with increasing passenger travel demand, high-speed railways have developed rapidly. The stop planning and timetabling problems are the core contents of high-speed railway transport planning and have important practical significance for improving efficiency of passenger travel and r...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10121017/ https://www.ncbi.nlm.nih.gov/pubmed/37083892 http://dx.doi.org/10.1371/journal.pone.0284747 |
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author | Li, Yawei Han, Baoming Zhao, Peng Yang, Ruixia |
author_facet | Li, Yawei Han, Baoming Zhao, Peng Yang, Ruixia |
author_sort | Li, Yawei |
collection | PubMed |
description | In recent years, with increasing passenger travel demand, high-speed railways have developed rapidly. The stop planning and timetabling problems are the core contents of high-speed railway transport planning and have important practical significance for improving efficiency of passenger travel and railway operation Dong et al. (2020). This study proposes a collaborative optimization approach that can be divided into two phases. In the first phase, a mixed-integer nonlinear programming model is constructed to obtain a stop plan by minimizing the total passenger travel time. The constraints of passenger origin-destination (OD) demand, train capacity, and stop frequency are considered in the first phase. In the second phase, the train timetable is optimized after the stop plan is obtained. A multiobjective mixed-integer linear optimization model is formulated by minimizing the total train travel time and the deviation between the expected and actual departure times from the origin station for all trains. Multiple types of trains and more refined headways are considered in the timetabling model. Finally, the approach is applied to China’s high-speed railway, and the GUROBI optimizer is used to solve the models in the above two stages. By analyzing the results, the total passenger travel time and train travel time decreased by 2.81% and 3.34% respectively. The proposed method generates a more efficient solution for the railway system. |
format | Online Article Text |
id | pubmed-10121017 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-101210172023-04-22 Collaborative optimization for train stop planning and train timetabling on high-speed railways based on passenger demand Li, Yawei Han, Baoming Zhao, Peng Yang, Ruixia PLoS One Research Article In recent years, with increasing passenger travel demand, high-speed railways have developed rapidly. The stop planning and timetabling problems are the core contents of high-speed railway transport planning and have important practical significance for improving efficiency of passenger travel and railway operation Dong et al. (2020). This study proposes a collaborative optimization approach that can be divided into two phases. In the first phase, a mixed-integer nonlinear programming model is constructed to obtain a stop plan by minimizing the total passenger travel time. The constraints of passenger origin-destination (OD) demand, train capacity, and stop frequency are considered in the first phase. In the second phase, the train timetable is optimized after the stop plan is obtained. A multiobjective mixed-integer linear optimization model is formulated by minimizing the total train travel time and the deviation between the expected and actual departure times from the origin station for all trains. Multiple types of trains and more refined headways are considered in the timetabling model. Finally, the approach is applied to China’s high-speed railway, and the GUROBI optimizer is used to solve the models in the above two stages. By analyzing the results, the total passenger travel time and train travel time decreased by 2.81% and 3.34% respectively. The proposed method generates a more efficient solution for the railway system. Public Library of Science 2023-04-21 /pmc/articles/PMC10121017/ /pubmed/37083892 http://dx.doi.org/10.1371/journal.pone.0284747 Text en © 2023 Li et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Li, Yawei Han, Baoming Zhao, Peng Yang, Ruixia Collaborative optimization for train stop planning and train timetabling on high-speed railways based on passenger demand |
title | Collaborative optimization for train stop planning and train timetabling on high-speed railways based on passenger demand |
title_full | Collaborative optimization for train stop planning and train timetabling on high-speed railways based on passenger demand |
title_fullStr | Collaborative optimization for train stop planning and train timetabling on high-speed railways based on passenger demand |
title_full_unstemmed | Collaborative optimization for train stop planning and train timetabling on high-speed railways based on passenger demand |
title_short | Collaborative optimization for train stop planning and train timetabling on high-speed railways based on passenger demand |
title_sort | collaborative optimization for train stop planning and train timetabling on high-speed railways based on passenger demand |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10121017/ https://www.ncbi.nlm.nih.gov/pubmed/37083892 http://dx.doi.org/10.1371/journal.pone.0284747 |
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