A Disturbance-Driven Textual Model for Train Running Time Prediction on High-Speed Railways-Reference-Cited by-同舟云学术

A Disturbance-Driven Textual Model for Train Running Time Prediction on High-Speed Railways

Published:2024-10 Issue:10 Volume:150 Page:
ISSN:2473-2907
Container-title:Journal of Transportation Engineering, Part A: Systems
language:en
Short-container-title:J. Transp. Eng., Part A: Systems

Author:

Pang Zishuai¹^ORCID,Wang Liwen²,Schonfeld Paul M.³,Liu Jie⁴^ORCID,Peng Qiyuan⁵,Li Li⁶

Affiliation:

1. School of Transportation and Logistics, Southwest Jiaotong Univ., Chengdu 610031, China. ORCID: .

2. School of Transportation and Logistics, Southwest Jiaotong Univ., Chengdu 610031, China.

3. Professor, Dept. of Civil and Environmental Engineering, Univ. of Maryland, College Park, MD 20742.

4. Associate Professor, Faculty of Transportation Engineering, Kunming Univ. of Science and Technology, Kunming 650093, China; Associate Researcher, Dept. of Engineering, Univ. of Cambridge, Cambridge CB2 1TN, UK (corresponding author). ORCID: .

5. Professor, School of Transportation and Logistics, Southwest Jiaotong Univ., Chengdu 610031, China.

6. Associate Professor, School of Transportation and Logistics, Southwest Jiaotong Univ., Chengdu, Sichuan 610031, China.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JTEPBS.TEENG-8389

Reference41 articles.

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3. Prediction of arrival times of freight traffic on US railroads using support vector regression

4. Stochastic prediction of train delays in real-time using Bayesian networks;Corman F.;Transp. Res. Part C Emerging Technol.,2018

5. Ding X. X. Xu J. Li and R. Shi. 2021. “A train delays prediction model under different causes based on MTGNN Approach.” In Proc. 2021 IEEE Int. Intelligent Transportation Systems Conf. (ITSC) 2387–2392. New York: IEEE.