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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This study developed a biobjective optimization model for planning maintenance and renewal actions related to track geometry in a railway network. The problem was modeled as a biobjective integer optimization problem from the perspective of the infrastructure manager. Two objective functions were minimized: (a) the total costs of planned maintenance and renewal actions and (b) the total number of train delays caused by speed restrictions. A small example for a simple network was analyzed in which the optimal Pareto frontier was found through a simulated annealing technique.

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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.