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Statistical modelling of railway track geometry degradation using Hierarchical Bayesian models

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  • Andrade, A.R.
  • Teixeira, P.F.

Abstract

Railway maintenance planners require a predictive model that can assess the railway track geometry degradation. The present paper uses a Hierarchical Bayesian model as a tool to model the main two quality indicators related to railway track geometry degradation: the standard deviation of longitudinal level defects and the standard deviation of horizontal alignment defects. Hierarchical Bayesian Models (HBM) are flexible statistical models that allow specifying different spatially correlated components between consecutive track sections, namely for the deterioration rates and the initial qualities parameters. HBM are developed for both quality indicators, conducting an extensive comparison between candidate models and a sensitivity analysis on prior distributions. HBM is applied to provide an overall assessment of the degradation of railway track geometry, for the main Portuguese railway line Lisbon–Oporto.

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  • Andrade, A.R. & Teixeira, P.F., 2015. "Statistical modelling of railway track geometry degradation using Hierarchical Bayesian models," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 169-183.
    • Handle: RePEc:eee:reensy:v:142:y:2015:i:c:p:169-183
    DOI: 10.1016/j.ress.2015.05.009
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    11. Cárdenas-Gallo, Iván & Sarmiento, Carlos A. & Morales, Gilberto A. & Bolivar, Manuel A. & Akhavan-Tabatabaei, Raha, 2017. "An ensemble classifier to predict track geometry degradation," Reliability Engineering and System Safety, Elsevier, vol. 161(C), pages 53-60.
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