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A high-speed railway network dataset from train operation records and weather data
High-speed train operation data are reliable and rich resources in data-driven research. However, the data released by railway companies are poorly organized and not comprehensive enough to be applied directly and effectively. A public high-speed railway network dataset suitable for research is stil...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9142585/ https://www.ncbi.nlm.nih.gov/pubmed/35624183 http://dx.doi.org/10.1038/s41597-022-01349-8 |
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author | Zhang, Dalin Peng, Yunjuan Xu, Yi Du, Chenyue Zhang, Yumei Wang, Nan Chong, Yunhao Wang, Hongwei Wu, Daohua Liu, Jintao Zhang, Hailong Lu, Lingyun Liu, Jiqiang |
author_facet | Zhang, Dalin Peng, Yunjuan Xu, Yi Du, Chenyue Zhang, Yumei Wang, Nan Chong, Yunhao Wang, Hongwei Wu, Daohua Liu, Jintao Zhang, Hailong Lu, Lingyun Liu, Jiqiang |
author_sort | Zhang, Dalin |
collection | PubMed |
description | High-speed train operation data are reliable and rich resources in data-driven research. However, the data released by railway companies are poorly organized and not comprehensive enough to be applied directly and effectively. A public high-speed railway network dataset suitable for research is still lacking. To support the research in large-scale complex network, complex dynamic system and intelligent transportation, we develop a high-speed railway network dataset, containing the train operation data in different directions from October 8, 2019 to January 27, 2020, the train delay data of the railway stations, the junction stations data, and the mileage data of adjacent stations. In the dataset, weather, temperature, wind power and major holidays are considered as factors affecting train operation. Potential research values of the dataset include but are not limited to complex dynamic system pattern mining, community detection and discovery, and train delay analysis. Besides, the dataset can be used to solve various railway operation and management problems, such as passenger service network improvement, train real-time dispatching and intelligent driving assistance. |
format | Online Article Text |
id | pubmed-9142585 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-91425852022-05-29 A high-speed railway network dataset from train operation records and weather data Zhang, Dalin Peng, Yunjuan Xu, Yi Du, Chenyue Zhang, Yumei Wang, Nan Chong, Yunhao Wang, Hongwei Wu, Daohua Liu, Jintao Zhang, Hailong Lu, Lingyun Liu, Jiqiang Sci Data Data Descriptor High-speed train operation data are reliable and rich resources in data-driven research. However, the data released by railway companies are poorly organized and not comprehensive enough to be applied directly and effectively. A public high-speed railway network dataset suitable for research is still lacking. To support the research in large-scale complex network, complex dynamic system and intelligent transportation, we develop a high-speed railway network dataset, containing the train operation data in different directions from October 8, 2019 to January 27, 2020, the train delay data of the railway stations, the junction stations data, and the mileage data of adjacent stations. In the dataset, weather, temperature, wind power and major holidays are considered as factors affecting train operation. Potential research values of the dataset include but are not limited to complex dynamic system pattern mining, community detection and discovery, and train delay analysis. Besides, the dataset can be used to solve various railway operation and management problems, such as passenger service network improvement, train real-time dispatching and intelligent driving assistance. Nature Publishing Group UK 2022-05-27 /pmc/articles/PMC9142585/ /pubmed/35624183 http://dx.doi.org/10.1038/s41597-022-01349-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Zhang, Dalin Peng, Yunjuan Xu, Yi Du, Chenyue Zhang, Yumei Wang, Nan Chong, Yunhao Wang, Hongwei Wu, Daohua Liu, Jintao Zhang, Hailong Lu, Lingyun Liu, Jiqiang A high-speed railway network dataset from train operation records and weather data |
title | A high-speed railway network dataset from train operation records and weather data |
title_full | A high-speed railway network dataset from train operation records and weather data |
title_fullStr | A high-speed railway network dataset from train operation records and weather data |
title_full_unstemmed | A high-speed railway network dataset from train operation records and weather data |
title_short | A high-speed railway network dataset from train operation records and weather data |
title_sort | high-speed railway network dataset from train operation records and weather data |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9142585/ https://www.ncbi.nlm.nih.gov/pubmed/35624183 http://dx.doi.org/10.1038/s41597-022-01349-8 |
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