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.
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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: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds) Recent Trends in Applied Artificial Intelligence. IEA/AIE 2013. Lecture Notes in Computer Science(), vol 7906. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38577-3_70
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DOI: https://doi.org/10.1007/978-3-642-38577-3_70
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