Algorithm to Forecast Railway Track Assets Performance in Europe

Railway track assets may suffer from different types of degradation due to aging, traffic conditions, environmental conditions, and natural and man-made hazards, which affect their performance in terms of reliability and availability, as well as passenger safety and comfort. By knowing which variables influence the degradation and performance of railway tracks, and the most appropriate maintenance and renewal actions, it is possible to define the most appropriate Performance Indicators. The use of predictive models to forecast these indicators can support the decision-making process during the maintenance management over time. In this work, a proposal including the selection of the most appropriate Performance Indicators is presented, together with a brief overview of predictive models used for railway systems. Based on that, a holistic framework to forecast the railway track performance aiming to support the decision-making process is given and its applicability is discussed. The proposed framework integrates the selection, processing, and aggregation of different types of Performance Indicators within a predictive modelling framework, enabling the analysis even when data availability is limited. The applicability of the framework is demonstrated through an illustrative example based on inspection data. The results illustrate the evolution of track condition states over time within a probabilistic framework.