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.