This research was devoted to optimising opportunistic tamping scheduling to present a cost-effective approach that considers both preventive and corrective tamping activities. To achieve this, we formulated the track geometry tamping scheduling problem as a mixed integer linear programming model and employed a genetic algorithm for its resolution. Key track quality indicators, including the standard deviation of the longitudinal level and single defects, were considered.We developed predictive models for the evolution of standard deviation and single defects over time, which were utilised to schedule preventive tamping activities and anticipate potential corrective actions. Additionally, we investigated the impact of both preventive and corrective tamping activities on the values of standard deviation and single defects.A case study on data from the Main Western Line in Sweden demonstrated that the fixed cost of occupying each maintenance window significantly influenced the total tamping cost. Moreover, the maintenance cycle interval notably affected the number of required corrective tamping activities. Specifically, a 3-month interval led to over 50 % fewer corrective tamping activities when compared to a 9-month interval. The results revealed that a 6-month interval achieved a favourable balance between corrective and preventive tamping activities and the total cost in our case study.

This study aimed to develop a multi-objective approach for reducing the positional errors in geometry measurements of track as a linear asset. Accordingly, we evaluated and compared two alignment methods – recursive segment-wise peak alignment (RSPA) and modified correlation optimised warping (MCOW). Furthermore, a novel rule-based approach was introduced to avoid data loss while aligning the datasets of the measurements of linear assets. A case study was conducted to implement and assess the performance of these methods in reducing the positional errors in track geometry measurements. The results revealed that the rule-based method preserves all the single defects present in the datasets. Furthermore, RSPA outperforms MCOW when aligning peaks, whereas MCOW is more efficient when all the data points in the datasets have equal priority.

Railway track, as a critical infrastructure, plays a significant role in freight transportation. However, Railway track degrades with age and usage and can impact negatively track availability and safety. Tamping actions are used to rejuvenate the degradation and recover the functionality of the track to an acceptable level. Tamping actions are performed in a form of preventive and corrective regimes. In performing an effective tamping regime, the recovery of both preventive and corrective tamping should be taken into account. In addition, the occurrence of isolated defects should be considered. By combining the recovery model with the degradation model, the long-term behavior of the track geometry can be predicted, and an accurate estimation of tamping needs can be provided, leading to optimum tamping scheduling. In this study, the effects of tamping recovery are modeled for both preventive and corrective strategies. For this aim, the values of both standard deviation (SD) and isolated defects have been predicted and their values before tamping are used as explanatory variables in a multivariable regression model. Finally, the effect of tamping recovery on the values of both SD and isolated defects is estimated. A case study is performed on a heavy haul line located in Sweden’s rail network to evaluate the performance of the proposed multivariable regression model. Observations showed that the model and its coefficients are significant with P-values close to zero, and the R-squared value suggests that the model explains approximately 70% of the variability in the response variable recovery.

The railway infrastructure plays a pivotal role in fostering economic growth and poverty alleviation. Over time, these infrastructures deteriorate due to aging and usage, compromising their functionality. Given their crucial socioeconomic significance and vast scale, ensuring their functionality and availability is paramount. Thus, an effective condition-based maintenance program is essential to restore reliability, facilitate cost-effective restoration, and enable continued benefits.

Track is a critical railway component susceptible to degradation from traffic loading, resulting in deviations from designated geometry parameters. Such degradation jeopardizes safety, availability, and travel quality. Developing an effective tamping regime emerges as a vital maintenance measure to control degradation and restore track geometry to acceptable standards. An optimal maintenance schedule becomes imperative to minimize costs, enhance track availability and capacity, and ensure safety.

Achieving efficient tamping scheduling necessitates accurate prediction of geometry degradation, accounting for tamping effects, and modeling the evolution of single defects. However, uncontrolled shifts in geometry measurements from different inspections—known as positional errors—can misplace defects and distort their evolution analysis. Therefore, precise alignment of geometry measurements is vital to eliminate such positional errors.

The purpose of this research was to streamline maintenance scheduling through leveraging track geometry measurements for modeling and prediction. Firstly, a study addressed alignment through evaluating and comparing four methods—Cross-Correlation Function (CCF), Recursive alignment by fast Fourier transform (RAFFT), Correlation optimized warping (COW), and Dynamic Time Warping (DTW). Furthermore, a combined RAFFT-COW method was proposed, overcoming their limitations. Comparison revealed COW aligned datasets satisfactorily without altering their shape but could not align endpoints precisely. The combined method effectively aligned datasets even when the datasets were stretched or compressed. Secondly, a modified COW (MCOW) addressed accurate and efficient alignment. MCOW surpassed COW's restrictions and reduced alignment time. To enhance robustness, MCOW with channel fusion (MFCOW) combined data from different channels, significantly reducing positional errors. Thirdly, a multi-objective approach proposed aimed at reducing positional errors in geometry measurements of track as a linear asset. Accordingly, recursive segment-wise peak alignment (RSPA) and MCOW were evaluated and compared. Furthermore, a novel rule-based approach proposed which prevented data loss during alignment, preserving all the single defects. In addition, the results revealed that RSPA excelled in aligning peaks, while MCOW proved efficient for datasets with equal priority data points.

Finally, an optimization model minimized track geometry maintenance costs through tamping scheduling. Key track quality indicators, including the standard deviation of the longitudinal level and single defects and the impact of preventive/corrective tamping on these indicators were integrated. Results showcased the influence of fixed maintenance window costs and maintenance cycle intervals on tamping expenses. The model's validity was confirmed through interactions with experienced practitioners from prominent railway infrastructure and maintenance entities.

This paper proposes a modification to a well-known alignment method, correlation optimized warping (COW), to improve the efficiency of the method and reduce the positional errors in the measurements of linear assets. The modified method relaxes the restrictions of COW in aligning the start and end of datasets and decreases the computational time. Furthermore, the method takes advantage of the interdependencies between simultaneously measured channels to overcome the missing data problem. A case study on railway track geometry measurements was conducted to implement the proposed method and assess its performance in reducing the positioning inaccuracy of the measurements. The findings revealed that the modified method could decrease the positional errors of defects to below 25 cm in 94% of the trials.

To predict the occurrence of geometry defects and to achieve a reliable maintenance strategy, accurate positioning of track geometry measurements is of great importance. This paper aims to reduce the positional errors in track geometry measurements by finding an efficient alignment method. Therefore, five alignment methods, i.e. the cross-correlation function, recursive alignment by fast Fourier transform, dynamic time warping, correlation optimized warping, and a combined method, were evaluated and compared concerning their ability to align the measurements precisely, keep the original shape of the measurements, and minimise the use of time and memory. Furthermore, the influence of choosing a proper reference dataset was investigated. A case study based on track geometry data from the Main Western Line in Sweden was conducted to implement and assess the methods. Findings revealed that the combined method could decrease the positional errors of single defects to below 0.25 m in 90% of the trials.

A great part of the cost of railway maintenance is related to the track geometry. Reliable and complete track geometry data form the foundation of an efficient and effective condition-based maintenance strategy. Generally, track geometry measurement data suffer from positional errors. Because the aim of track geometry degradation modeling has been to analyze the evolution of location-specific defects over time, accurate information on the position of defects is of crucial importance. Therefore, collected track geometry measurements in different inspection runs need to be pre-processed before they are used to model geometry degradation or implement a condition-based maintenance strategy. In this study, three relative position-based approaches are applied to align the positional measurement data obtained from different inspection runs. The two main types of data positional errors are shifted and stretched waveforms. In this regard, three time series alignment algorithms, i.e., crosscorrelation function, dynamic time warping, and dynamic time alignment kernel, are applied to align both shifted and stretched waveforms. Foot-by-foot track geometry data obtained from the main Western line in Sweden are used to implement and test the models. On the basis of the results, dynamic time warping outperforms the other two techniques to align shifted and stretched datasets.

This research is devoted to optimizing opportunistic tamping scheduling to present a practical and cost-effective approach that considers both preventive and corrective tamping activities. To accomplish this, we formulated the track geometry tamping scheduling problem as a mixed integer linear programming (MILP) model and employed a genetic algorithm for its resolution. Key track quality indicators, including the standard deviation of the longitudinal level and single defects in each segment, were considered.

We developed predictive models for the evolution of standard deviation and single defects over time, which were utilized to schedule preventive tamping activities and anticipate potential corrective actions. Additionally, we investigated the impact of both preventive and corrective tamping activities on the values of standard deviation and single defects.

A case study on data from the Main Western Line in Sweden demonstrated that the fixed cost for occupying each maintenance window significantly influenced the total tamping cost. Moreover, the maintenance cycle interval notably affected the number of required corrective tamping activities. Specifically, a 3-month interval led to over 50% fewer corrective tamping activities compared to a 9-month interval. The results revealed that a 6-month interval struck a favorable balance between corrective and preventive tamping activities and the total cost for our case study.