(Created page with " == 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 strategi...") |
m (Scipediacontent moved page Draft Content 325668965 to Liu et al 2020l) |
(No difference)
| |
Latest revision as of 10:07, 15 February 2021
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
Original document
The different versions of the original document can be found in:
- http://dx.doi.org/10.1155/2020/8974315 under the license https://creativecommons.org/licenses/by/4.0/
Document information
Published on 01/01/2020
Volume 2020, 2020
DOI: 10.1155/2020/8974315
Licence: Other
Share this document
Keywords
claim authorship
Are you one of the authors of this document?