Machine learning techniques to predict reactionary delays and other associated key performance indicators on British railway network-Reference-Cited by-同舟云学术

Machine learning techniques to predict reactionary delays and other associated key performance indicators on British railway network

Published:2020-12-31 Issue: Volume: Page:1-28
ISSN:1547-2450
Container-title:Journal of Intelligent Transportation Systems
language:en
Short-container-title:Journal of Intelligent Transportation Systems

Author:

Taleongpong Panukorn¹,Hu Simon²^ORCID,Jiang Zhoutong³,Wu Chao⁴,Popo-Ola Sunday¹,Han Ke⁵^ORCID

Affiliation:

1. Center for Transport Studies, Department of Civil and Environmental Engineering, Imperial College London, London, UK

2. School of Civil Engineering, Zhejiang University/University of Illinois at Urbana-Champaign Institute (ZJU-UIUC Institute), Haining, China

3. Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA

4. School of Public Affairs, Zhejiang University, Hangzhou, China

5. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China

Funder

Ministry of Science and Technology, China

National Key Research and Development Project of China

National Natural Science Foundation of China

Zhejiang University and Cybervein Joint Research Lab

Zhejiang Natural Science Foundation

Zhejiang University/University of Illinois at Urbana-Champaign Institute

Publisher

Informa UK Limited

Subject

Applied Mathematics,Computer Science Applications,Aerospace Engineering,Automotive Engineering,Information Systems,Software,Control and Systems Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/15472450.2020.1858822

Reference35 articles.

1. Bleakley, L., Akinola, A., Fullard, R. (2019, October). RDG information feeds developer pack. https://www.nationalrail.co.uk/46391.aspx

2. Bosscha, E. (2016). Big data in railway operations: Using artificial neural networks to predict train delay propagation [Unpublished doctoral dissertation]. University of Twente.

3. Stochastic modelling of delay propagation in large networks

4. XGBoost

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Bayesian Spatio-Temporal Graph Convolutional Network for Railway Train Delay Prediction;IEEE Transactions on Intelligent Transportation Systems;2024-07

2. Bag-of-Future: Time Series Forecasting by Continuous Function Candidates;2024 IEEE 33rd International Symposium on Industrial Electronics (ISIE);2024-06-18

3. Forecasting train arrival delays on the Ankara – Eskişehir high-speed line in Turkey;Journal of Rail Transport Planning & Management;2024-06

4. Analyzing factors contributing to real-time train arrival delays using seemingly unrelated regression models;Transportation Research Part A: Policy and Practice;2023-08

5. A review of data-driven approaches to predict train delays;Transportation Research Part C: Emerging Technologies;2023-03