Facilitating Maintenance Decisions on the Dutch Railways Using Big
Data: The ABA Case Study
Reference
A. Núñez,
J. Hendriks,
Z. Li,
B. De Schutter, and
R. Dollevoet,
"Facilitating Maintenance Decisions on the Dutch Railways Using Big
Data: The ABA Case Study," Proceedings of the 2014
IEEE International Conference on Big Data, Washington, DC, pp.
48-53, Oct. 2014.
Abstract
This paper discusses the applicability of Big Data techniques to
facilitate maintenance decisions regarding railway tracks. Currently,
in different countries, a huge amount of railway track
condition-monitoring data is being collected from different sources.
However, the data are not yet fully used because of the lack of
suitable techniques to extract the relevant events and crucial
historical information. Thus, valuable information is hidden behind a
huge amount of terabytes from different sensors. In this paper, the
conditions of the 5V's of Big Data (Volume, Velocity, Variety,
Veracity and Value) in railway monitoring systems are discussed. Then,
general methods that can be applied to facilitate the decision of
efficient railway track maintenance are proposed for railway track
condition monitoring. As a benchmark, axle box acceleration (ABA)
measurements in the Dutch tracks are used, and generic reduction
formulations to address new relevant information and handle failures
are proposed.
Downloads
- Corresponding technical report:
pdf
file
(910 KB)
Bibtex entry
@inproceedings{NunHen:15-014,
author={A. N{\'{u}}{\~{n}}ez and J. Hendriks and Z. Li and B. {D}e Schutter and
R. Dollevoet},
title={Facilitating Maintenance Decisions on the {Dutch} Railways Using Big
Data: {The} {ABA} Case Study},
booktitle={Proceedings of the 2014 IEEE International Conference on Big
Data},
address={Washington, DC},
pages={48--53},
month=oct,
year={2014}
}
This page is maintained by Bart De Schutter.
Last update: February 21, 2026.