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

doi: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

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

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

22 Citations (Scopus)

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 language	English
Title of host publication	Recent Trends in Applied Artificial Intelligence - 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Proceedings
Publisher	Springer-Verlag London Ltd.
Pages	674-683
Number of pages	10
Volume	7906 LNAI
ISBN (Print)	9783642385766
DOIs	https://doi.org/10.1007/978-3-642-38577-3_70
Publication status	Published - 2013
Externally published	Yes
Event	International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems 2013 - Amsterdam, Netherlands Duration: 17 Jun 2013 → 21 Jun 2013 Conference number: 26th http://dblp.uni-trier.de/db/conf/ieaaie/ieaaie2013.html

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7906 LNAI
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems 2013
Abbreviated title	IEA/AIE 2013
Country/Territory	Netherlands
City	Amsterdam
Period	17/06/13 → 21/06/13
Other	Old name (ERA List) = International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems
Internet address	http://dblp.uni-trier.de/db/conf/ieaaie/ieaaie2013.html

Keywords

machine learning
prediction
railway maintenance
time series
track maintenance

Access to Document

10.1007/978-3-642-38577-3_70

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 et al. / 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 et al.
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 proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - A study on the use of machine learning methods for incidence prediction in high-speed train tracks

AU - Bergmeir, Christoph

AU - Sáinz, Gregorio

AU - Martínez Bertrand, Carlos

AU - Benítez, José Manuel

N1 - Conference code: 26th

PY - 2013

Y1 - 2013

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.

KW - machine learning

KW - prediction

KW - railway maintenance

KW - time series

KW - track maintenance

UR - https://www.scopus.com/pages/publications/84881386356

U2 - 10.1007/978-3-642-38577-3_70

DO - 10.1007/978-3-642-38577-3_70

M3 - Conference Paper

AN - SCOPUS:84881386356

SN - 9783642385766

VL - 7906 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 674

EP - 683

BT - Recent Trends in Applied Artificial Intelligence - 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Proceedings

PB - Springer-Verlag London Ltd.

T2 - International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems 2013

Y2 - 17 June 2013 through 21 June 2013

ER -

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)). doi: 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

Abstract

Publication series

Conference

Keywords

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