LAUSR

Railway track geometry varies along routes depending on topographical, operational and safety constraints. Tracks are prone to degrade over time due to various factors, with deviations from the original geometry design having potential implications for comfort and safety. Regular inspections are carried out to evaluate track condition and determine whether maintenance interventions should be undertaken to correct track geometry. The dynamic measurement of track geometry parameters generates large volumes of data that must be analysed to evaluate track degradation. This work comprehensively explains how track quality is evaluated, introducing four main categories of factors affecting it. These are track design, loading, environment and maintenance. The most common techniques applied to evaluate track condition and predict degradation and faults, categorised into statistical, Machine Learning, Big Data and other, are also introduced. Specifically, the influence of each factor on track geometry is stated and the common techniques applied to each factor determined from this review. The utility of loading and maintenance data for fault prediction depend on the availability of records, whilst the impact of environmental conditions is expected to become increasingly important due to climate change. Artificial Neural Networks, Bayesian models and regression are the most applied techniques for determining track degradation behaviour and fault prediction, considering several different factors in their models. Increasingly sophisticated algorithms can consider multiple factors in tandem to predict faults based on the unique conditions of specified tracks.