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.

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.

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.

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.