Abstract

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.

Highlights

  • Train dwell time estimation is a critical issue for both the scheduling and rescheduling phases

  • The main methodology of this paper is as follows: first, a train dwell time estimation model is selected; second, the generality of the selected model is analyzed by using both comparison and theoretical approaches

  • We find that the actual dwell time fluctuates significantly, and the difference between the maximum and scheduled dwell times is larger than the difference between the minimum and scheduled dwell times

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Summary

Introduction

Train dwell time estimation is a critical issue for both the scheduling and rescheduling phases. One intuitive methodology is to apply the model to more or a wider range of independent cases; if the accuracies under these different cases are acceptable, the model can be classified as a relatively general model This approach is called a comparison approach. Comparison approaches may face problems like how many cases they should cover and what the wider range means Another methodology is to analyze the parameters and variables of the model theoretically to prove its generality [1], which is called a measure theoretical approach. The main methodology of this paper is as follows: first, a train dwell time estimation model is selected; second, the generality of the selected model is analyzed by using both comparison and theoretical approaches.

Literature Review
Model Description
Generality Analysis by Comparison Approach
Dataset Resources
Generality Analysis Using a Theoretical Approach
Generality Improvement
Discussion
Conclusions
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