Measuring the similarity (or distance) between trajectories of moving objects is a common procedure taken by most trajectory data-driven applications. One of the biggest challenges of trajectory distances measurement is that the distance needs to be carefully defined in order to reflect the true underlying similarity. This is due to the fact that trajectories are essentially non-uniform sequential data with variable length, attached with both spatial and temporal attributes, which may or may not be considered for similarity measures. Therefore, tens of similarity measures for trajectory data have been proposed; every technique claim an advantage over the others in a different aspect. Hence, it's difficult for users to choose the best-suited technique, as well as the appropriate parameter values, since each technique has distinct performance and characteristics depending on various factors. In this paper, we develop an application that allows to evaluate several techniques in different aspects (accuracy, sensitivity to trajectory features, performance, etc.). We believe that this tool will be able to serve as a practical guideline for both researchers and developers. While researchers can use our tool to assess existing or new techniques, developers can reuse its components to reduce the development complexity.