Train motion model calibration

Research agenda and practical recommendations

Conference Paper (2022)
Author(s)

A. Cunillera (TU Delft - Transport and Planning)

Nikola Bešinović (TU Delft - Transport and Planning)

Ramon Lentink (Nederlandse Spoorwegen)

N. van Oort (TU Delft - Transport and Planning)

R.M.P. Goverde (TU Delft - Transport and Planning)

Transport and Planning
Copyright
© 2022 A. Cunillera, Nikola Bešinović, Ramon Lentink, N. van Oort, R.M.P. Goverde
DOI related publication
https://doi.org/10.1109/ITSC55140.2022.9922542
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 A. Cunillera, Nikola Bešinović, Ramon Lentink, N. van Oort, R.M.P. Goverde
Transport and Planning
Pages (from-to)
1049-1054
ISBN (print)
978-1-6654-6881-7
ISBN (electronic)
978-1-6654-6880-0
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

An accurate train motion model is a key component of a wide spectrum of railway applications, from timetabling algorithms to Automatic Train Operation systems. Therefore, model calibration has become crucial in the railway industry, although this topic has not received the attention and recognition in academia that its practical relevance deserves. Several data-driven techniques have been devised to calibrate train dynamics models, although an overview that describes the current state of the art in the field and highlights the following steps to be researched is still missing in the literature. Thus, this article has four main goals. First, giving a brief insight into the broad variety of techniques used for train motion model calibration, focusing on those techniques that use on-board measurements and are applicable in railway operation. Second, highlighting the main research steps to be tackled, considering the current main challenges in railway research. Third, outlining practical recommendations to practitioners who need to calibrate their algorithms and applications. And fourth, contributing to giving train motion model calibration its due recognition.

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