Teetool – A Probabilistic Trajectory Analysis Tool. Issue 1 (17th May 2017)

• Record Type: Journal Article
• Authors: Eerland, Willem; Box, Simon; Fangohr, Hans; Sóbester, András
• Is Part Of: Journal of open research software. Volume 5:Issue 1(2017)
• Publication Date: 2017-05-17
• Subjects: Python -- Gaussian process -- motion patterns -- trajectory patterns -- confidence region; Computer software -- Reusability -- Periodicals; Open source software -- Periodicals; 005
• DOI: 10.5334/jors.163
• Languages: English
• ISSNs: 2049-9647

Abstract

Teetool is a Python package which models and visualises motion patterns found in two- and three-dimensional trajectory data. It models the trajectories as a Gaussian process and uses the mean and covariance of the trajectory data to produce a confidence region, an area (or volume) through which a given percentage of trajectories travel. The confidence region is useful in obtaining an understanding of, or quantifying, dispersion in trajectory data. Furthermore, by modelling the trajectories as a Gaussian process, missing data can be recovered and noisy measurements can be corrected. Teetool is available as a Python package on GitHub, and includes Jupyter Notebooks, showing examples for two- and three-dimensional trajectory data.