A study on the use of machine learning methods for incidence prediction in high-speed train tracks

Christoph Bergmeir, Gregorio Sáinz, Carlos Martínez Bertrand, José Manuel Benítez

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

In this paper a study of the application of methods based on Computational Intelligence (CI) procedures to a forecasting problem in railway maintenance is presented. Railway maintenance is an important and long-standing problem that is critical for safe, comfortable and economic transportation. With the advent of high-speed lines, the problem has even more importance nowadays. We have developed a study, applying forecasting procedures from Statistics and CI, to examine the feasibility of predicting one-month-ahead faults on two high-speed lines in Spain. The data are faults recorded by a measurement train which traverses the lines monthly. The results indicate that CI methods are competitive in this forecasting task against the Statistical regression methods, with ε-support vector regression outperforming the other employed methods. So, application of CI methods is feasible in this forecasting task and it is useful in the planning process of track maintenance.

Original languageEnglish
Title of host publicationRecent Trends in Applied Artificial Intelligence - 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Proceedings
PublisherSpringer-Verlag London Ltd.
Pages674-683
Number of pages10
Volume7906 LNAI
ISBN (Print)9783642385766
DOIs
Publication statusPublished - 2013
Externally publishedYes
EventInternational Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems 2013 - Amsterdam, Netherlands
Duration: 17 Jun 201321 Jun 2013
Conference number: 26th
http://dblp.uni-trier.de/db/conf/ieaaie/ieaaie2013.html

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7906 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

ConferenceInternational Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems 2013
Abbreviated titleIEA/AIE 2013
CountryNetherlands
CityAmsterdam
Period17/06/1321/06/13
OtherOld name (ERA List) = International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems
Internet address

Keywords

  • machine learning
  • prediction
  • railway maintenance
  • time series
  • track maintenance

Cite this

Bergmeir, C., Sáinz, G., Martínez Bertrand, C., & Benítez, J. M. (2013). A study on the use of machine learning methods for incidence prediction in high-speed train tracks. In Recent Trends in Applied Artificial Intelligence - 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Proceedings (Vol. 7906 LNAI, pp. 674-683). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7906 LNAI). Springer-Verlag London Ltd.. https://doi.org/10.1007/978-3-642-38577-3_70
Bergmeir, Christoph ; Sáinz, Gregorio ; Martínez Bertrand, Carlos ; Benítez, José Manuel. / A study on the use of machine learning methods for incidence prediction in high-speed train tracks. Recent Trends in Applied Artificial Intelligence - 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Proceedings. Vol. 7906 LNAI Springer-Verlag London Ltd., 2013. pp. 674-683 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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title = "A study on the use of machine learning methods for incidence prediction in high-speed train tracks",
abstract = "In this paper a study of the application of methods based on Computational Intelligence (CI) procedures to a forecasting problem in railway maintenance is presented. Railway maintenance is an important and long-standing problem that is critical for safe, comfortable and economic transportation. With the advent of high-speed lines, the problem has even more importance nowadays. We have developed a study, applying forecasting procedures from Statistics and CI, to examine the feasibility of predicting one-month-ahead faults on two high-speed lines in Spain. The data are faults recorded by a measurement train which traverses the lines monthly. The results indicate that CI methods are competitive in this forecasting task against the Statistical regression methods, with ε-support vector regression outperforming the other employed methods. So, application of CI methods is feasible in this forecasting task and it is useful in the planning process of track maintenance.",
keywords = "machine learning, prediction, railway maintenance, time series, track maintenance",
author = "Christoph Bergmeir and Gregorio S{\'a}inz and {Mart{\'i}nez Bertrand}, Carlos and Ben{\'i}tez, {Jos{\'e} Manuel}",
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Bergmeir, C, Sáinz, G, Martínez Bertrand, C & Benítez, JM 2013, A study on the use of machine learning methods for incidence prediction in high-speed train tracks. in Recent Trends in Applied Artificial Intelligence - 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Proceedings. vol. 7906 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7906 LNAI, Springer-Verlag London Ltd., pp. 674-683, International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems 2013, Amsterdam, Netherlands, 17/06/13. https://doi.org/10.1007/978-3-642-38577-3_70

A study on the use of machine learning methods for incidence prediction in high-speed train tracks. / Bergmeir, Christoph; Sáinz, Gregorio; Martínez Bertrand, Carlos; Benítez, José Manuel.

Recent Trends in Applied Artificial Intelligence - 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Proceedings. Vol. 7906 LNAI Springer-Verlag London Ltd., 2013. p. 674-683 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7906 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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AU - Martínez Bertrand, Carlos

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N2 - In this paper a study of the application of methods based on Computational Intelligence (CI) procedures to a forecasting problem in railway maintenance is presented. Railway maintenance is an important and long-standing problem that is critical for safe, comfortable and economic transportation. With the advent of high-speed lines, the problem has even more importance nowadays. We have developed a study, applying forecasting procedures from Statistics and CI, to examine the feasibility of predicting one-month-ahead faults on two high-speed lines in Spain. The data are faults recorded by a measurement train which traverses the lines monthly. The results indicate that CI methods are competitive in this forecasting task against the Statistical regression methods, with ε-support vector regression outperforming the other employed methods. So, application of CI methods is feasible in this forecasting task and it is useful in the planning process of track maintenance.

AB - In this paper a study of the application of methods based on Computational Intelligence (CI) procedures to a forecasting problem in railway maintenance is presented. Railway maintenance is an important and long-standing problem that is critical for safe, comfortable and economic transportation. With the advent of high-speed lines, the problem has even more importance nowadays. We have developed a study, applying forecasting procedures from Statistics and CI, to examine the feasibility of predicting one-month-ahead faults on two high-speed lines in Spain. The data are faults recorded by a measurement train which traverses the lines monthly. The results indicate that CI methods are competitive in this forecasting task against the Statistical regression methods, with ε-support vector regression outperforming the other employed methods. So, application of CI methods is feasible in this forecasting task and it is useful in the planning process of track maintenance.

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Bergmeir C, Sáinz G, Martínez Bertrand C, Benítez JM. A study on the use of machine learning methods for incidence prediction in high-speed train tracks. In Recent Trends in Applied Artificial Intelligence - 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Proceedings. Vol. 7906 LNAI. Springer-Verlag London Ltd. 2013. p. 674-683. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-38577-3_70