Hierarchical Bayesian modelling of rail track geometry degradation

Abstract

This paper explores hierarchical Bayesian models that can be used to predict rail track geometry degradation and thus guide planning maintenance and renewal actions. Hierarchical Bayesian models allow great flexibility in their specification, especially if they are combined with conditional autoregressive terms that can take into account spatial dependencies between model parameters. For rail track geometry degradation, conditional autoregressive terms are specified to tackle spatial interactions between consecutive rail track sections in rail track lines. An analysis of inspection, operation and maintenance data from the main Portuguese line (Lisbon–Oporto) motivates and illustrates the proposed predictive models. Inference is then conducted based on Markov Chain Monte Carlo (MCMC) simulation, which is proposed for fitting different model specifications. Finally, model comparison and a sensitivity analysis on prior distribution parameters are assessed.