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Journal of Advanced Transportation
Volume 2018 (2018), Article ID 8521576, 16 pages
Research Article

Testing the Generality of a Passenger Disregarded Train Dwell Time Estimation Model at Short Stops: Both Comparison and Theoretical Approaches

1State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
2Guangzhou Railway Polytechnic, Guangzhou, Guangdong 510430, China

Correspondence should be addressed to Dewei Li

Received 15 June 2017; Revised 1 November 2017; Accepted 11 January 2018; Published 15 February 2018

Academic Editor: Zhi-Chun Li

Copyright © 2018 Dewei Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Train dwell time estimation is a critical issue in both scheduling and rescheduling phases. In a previous paper, the authors proposed a novel dwell time estimation model at short stops which did not require the passenger data. This model shows promising results when applied to Dutch railway stations. This paper focuses on testing and improving the generality of the model by two steps: first, the model is tested by applying more independent datasets from another city and comparing the estimation accuracy with the previous Dutch case; second, the model’s generality is tested by a theoretical approach through the analysis of individual model parameters, variables, model scenarios, and model structure as well as work conditions. The validation results during peak hours show that the MAPE of the model is 11.4%, which is slightly better than the results for the Dutch railway stations. A more generalized predictor called “dwell time at the associated station” is used to replace the square root term in the original model. The improved model can estimate train dwell time in all the investigated stations during both peak and off-peak periods. We conclude that the proposed train dwell time estimation model is generic in the given condition.