An ensemble classifier to predict track geometry degradation

Cárdenas-Gallo, Iván, Carlos A. Sarmiento, Gilberto A. Morales, Manuel A. Bolivar and Raha Akhavan-Tabatabaei

Reliability Engineering and System Safety, 2017, vol. 161, issue C, 53-60

Abstract: Railway operations are inherently complex and source of several problems. In particular, track geometry defects are one of the leading causes of train accidents in the United States. This paper presents a solution approach which entails the construction of an ensemble classifier to forecast the degradation of track geometry. Our classifier is constructed by solving the problem from three different perspectives: deterioration, regression and classification. We considered a different model from each perspective and our results show that using an ensemble method improves the predictive performance.

Keywords: Railroad maintenance; Defects; Gamma process; Logistic regression; Support vector machines; Classification; Ensemble algorithms (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:161:y:2017:i:c:p:53-60

DOI: 10.1016/j.ress.2016.12.012

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