Tools
Tools
Zhang, Q, Liu, X, Spurgeon, S and Yu, DL (2021) A two-layer modelling framework for predicting passenger flow on trains: A case study of London underground trains. Transportation Research Part A: Policy and Practice, 151. pp. 119-139. ISSN 0965-8564
|
Text
Manuscript_TRPA.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview |
Abstract
A model that anticipates the passenger flow on trains will help passengers to avoid overcrowded trains in their journey planning. Such a model will also help rail industry to understand the current use of train capacity and plan the distribution of rolling stock, personnel and facilities. However, the existing studies only developed the models for forecasting the passenger flow in stations, which cannot reflect the true passenger number on trains. In this paper, a hierarchical modelling framework for passenger flow prediction is proposed. It includes two layers of fuzzy models, where a global model is used to predict for ordinary circumstances and a number of local models are used to predict the variations in passenger number due to specific factors, such as events and weather. A new data sifting method is proposed to obtain the most informative and representative data for model training, which greatly improves the modelling efficiency. The proposed method is then validated using a case study of forecasting the passenger flow of London Underground trains.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 1205 Urban and Regional Planning, 1507 Transportation and Freight Services |
| Subjects: | H Social Sciences > HE Transportation and Communications Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > TF Railroad engineering and operation |
| Divisions: | Engineering |
| Publisher: | Elsevier |
| Date Deposited: | 24 Aug 2021 09:47 |
| Last Modified: | 29 Jul 2022 00:50 |
| DOI or ID number: | 10.1016/j.tra.2021.07.001 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/15401 |
 |
View Item |