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A Study on the Use of Machine Learning Methods for Incidence Prediction in High-Speed Train Tracks

机译：机器学习方法在高速列车轨道事故预测中的应用研究

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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.
机译：本文对基于计算智能（CI）程序的方法在铁路维修预测问题中的应用进行了研究。铁路维护是一个重要而长期的问题，对于安全，舒适和经济的运输至关重要。随着高速线的出现，如今的问题变得更加重要。我们已经进行了一项研究，应用了Statistics和CI的预测程序，以检验在西班牙的两条高速线上预测一个月故障的可行性。数据是由测量列车记录的故障，测量列车每月穿越线路。结果表明，CI方法在此预测任务中与统计回归方法相比具有竞争优势，其中ε-支持向量回归优于其他方法。因此，在此预测任务中应用CI方法是可行的，并且在轨道维护的计划过程中很有用。

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