Increased demand for railway transportation is creating a need for higher train speeds and axle loads. These, in turn, increase the likelihood of track degradation and failures. Modelling the degradation behaviour of track geometry and development of applicable and effective maintenance strategies has become a challenging concern for railway infrastructure managers. During the last three decades, a number of track geometry degradation and maintenance modelling approaches have been developed to predict and improve the railway track geometry condition. In this paper, existing track geometry measures are identified and discussed. Available models for track geometry degradation are reviewed and classified. Tamping recovery models are also reviewed and discussed to identify the issues and challenges of different available methodologies and models. Existing track geometry maintenance models are reviewed and critical observations on each contribution are provided. The most important track maintenance scheduling models are identified and discussed. Finally, the paper provides directions for further research.

The aim of this study has been to develop a data-driven analytical methodology for prediction of isolated track geometry defects, based on the measurement data obtained from a field study. Within the study, a defect-based model has been proposed to identify the degradation pattern of isolated longitudinal level defects. The proposed model considered the occurrence of shock events in the degradation path. Furthermore, the effectiveness of tamping intervention in rectifying the longitudinal level defects was analysed. The results show that the linear model is an appropriate choice for modelling the degradation pattern of longitudinal level defects. In addition, a section-based model has been developed using binary logistic regression to predict the probability of occurrence of isolated defects associated with track sections. The model considered the standard deviation and kurtosis of longitudinal level as explanatory variables. It has been found that the kurtosis of the longitudinal level is a statistically significant predictor of the occurrence of isolated longitudinal level defects in a given track section. The validation results show that the proposed binary logistic regression model can be used to predict the occurrence of isolated defects in a track section.

Accurate prediction and efficient simulation of the evolution of track geometry condition is a prerequisite for planning effective railway track maintenance. In this regard, the degradation and tamping effect should be equipped with proper and efficient probabilistic models. The possible correlation induced by the spatial structure also needs to be taken into account when modelling the track geometry degradation. To address these issues, a two-level piecewise linear model is proposed to model the degradation path. At the first level, the degradation characteristic of each track section is modelled by a piecewise linear model with known break points at the tamping times. At the second level, Autoregressive Moving Average models are used to capture the spatial dependences between the parameters of the regression lines indexed by their locations. To illustrate the model, a comprehensive case study is presented using data from the Main Western Line in Sweden.

Tamping is one of the major activities undertaken by railway maintenance managers to recover the track geometry condition. Modelling the effectiveness of tamping along with track geometry degradation is essential for long-term prediction of track geometry behaviour. The aim of this study is to analyse the effect of tamping on the different track geometry measurements, i.e. longitudinal level, alignment and cant, based on inspection car records from a part of the Main Western Line in Sweden. To model recovery after tamping, a probabilistic approach is applied. The track geometry condition before tamping was considered as the dominant factor for modelling the model parameters. Correlation analysis was performed to measure the linear relation between the recoveries of the different geometry measures. The results show a moderate correlation between the recovery of the longitudinal level and that of the cant, and a weak correlation between the recovery of the longitudinal level and that of the alignment. Linear regression and Wiener process were also applied to model track geometry degradation and to obtain degradation rates. The effect of tamping on degradation rate was analysed. It was observed that degradation rate increased after tamping.

In order to evaluate the railway track geometry condition and plan maintenance activities, track inspection cars run over the track at specific times to monitor it and record geometry measurements. Applying an adequate inspection interval is vital to ensure the availability, safety and quality of the railway track, at the lowest possible cost. The aim of this study has been to investigate the effect of different inspection intervals on the track geometry condition. To achieve this, an integrated statistical model was developed to predict the track geometry condition given different inspection intervals. In order to model the evolution of the track geometry condition, a piecewise exponential model was used which considers break points at the maintenance times. Ordinal logistic regression was applied to model the probability of the occurrence of severe isolated defects. The Monte Carlo technique was used to simulate the track geometry behaviour given different inspection intervals. The results of the proposed model support the decision-making process regarding the selection of the most adequate inspection interval. The applicability of the model was tested in a case study on the Main Western Line in Sweden.