== Abstract ==

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As a critical foundation for train traffic management, a train stop plan is associated with several other plans in high-speed railway train operation strategies. The current approach to train stop planning in China is based primarily on passenger demand volume information and the preset high-speed railway station level. With the goal of efficiently optimising the stop plan, this study proposes a novel method that uses machine learning techniques without a predetermined hypothesis and a complex solution algorithm. Clustering techniques are applied to assess the features of the service nodes (e.g., the station level). A modified Markov decision process (MDP) is conducted to express the entire stop plan optimisation process considering several constraints (service frequency at stations and number of train stops). A restrained MDP-based stop plan model is formulated, and a numerical experiment is conducted to demonstrate the performance of the proposed approach with real-world train operation data collected from the Beijing-Shanghai high-speed railway.

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Document type: Article

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== Full document ==

<pdf>Media:Draft_Content_325668965-beopen664-6916-document.pdf</pdf>

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== Original document ==

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The different versions of the original document can be found in:

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* [http://dx.doi.org/10.1155/2020/8974315 http://dx.doi.org/10.1155/2020/8974315] under the license https://creativecommons.org/licenses/by/4.0/

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* [http://downloads.hindawi.com/journals/jat/2020/8974315.pdf http://downloads.hindawi.com/journals/jat/2020/8974315.pdf],

: [http://downloads.hindawi.com/journals/jat/2020/8974315.xml http://downloads.hindawi.com/journals/jat/2020/8974315.xml],

: [http://dx.doi.org/10.1155/2020/8974315 http://dx.doi.org/10.1155/2020/8974315]

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* [http://dx.doi.org/10.1155/2020/8974315 http://dx.doi.org/10.1155/2020/8974315],

: [https://doaj.org/toc/0197-6729 https://doaj.org/toc/0197-6729],

: [https://doaj.org/toc/2042-3195 https://doaj.org/toc/2042-3195] under the license https://creativecommons.org/licenses/by/4.0/

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* [https://www.hindawi.com/journals/jat/2020/8974315 https://www.hindawi.com/journals/jat/2020/8974315],

: [https://academic.microsoft.com/#/detail/3095823701 https://academic.microsoft.com/#/detail/3095823701]

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