Abstract

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.

Document type: Article

Full document

The PDF file did not load properly or your web browser does not support viewing PDF files. Download directly to your device: Download PDF document

Original document

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

http://downloads.hindawi.com/journals/jat/2020/8974315.xml,
http://dx.doi.org/10.1155/2020/8974315
https://doaj.org/toc/0197-6729,
https://doaj.org/toc/2042-3195 under the license https://creativecommons.org/licenses/by/4.0/
https://academic.microsoft.com/#/detail/3095823701
Back to Top

Document information

Published on 01/01/2020

Volume 2020, 2020
DOI: 10.1155/2020/8974315
Licence: Other

Document Score

0

Views 0
Recommendations 0

Share this document

claim authorship

Are you one of the authors of this document?