ginlph / trajectory_distance

trajectory_distance

=====================

trajectory_distance is a Python module for computing distance between trajectory objects. It is implemented in both Python and Cython.

Description

trajectory_distance contains 9 distances between trajectory.

1. SSPD (Symmetric Segment-Path Distance)
2. OWD (One-Way Distance)
3. Hausdorff
4. Frechet
5. Discret Frechet
6. DTW (Dynamic Time Warping)
7. LCSS (Longuest Common Subsequence)
8. ERP (Edit distance with Real Penalty)
9. EDR (Edit Distance on Real sequence)

Dependencies

trajectory_distance is tested to work under Python 2.7.

The required dependencies to build the software are NumPy >= 1.9.1, Cython >= 0.21.2, shapely >= 1.5.6, Geohash and a working C/C++ compiler.

Install

This package uses distutils. To install in your home directory, use:

python setup.py install 

How to use it

You only need to import the distance module.

import traj_dist.distance as tdist

All distances are in this module. There is also two extra function 'cdist', and 'pdist' to compute distances between all trajectories in a list.

Trajectory should be represented as 2-Dimensions numpy array. See traj_dist/example.py file.

Some distance requires extra-parameters. See the help function for more information about how to use each distance.

References

1. P. Besse, B. Guillouet, J.-M. Loubes, and R. Francois, “Review and perspective for distance based trajectory clustering,” arXiv preprint arXiv:1508.04904, 2015.
2. B. Lin and J. Su, “Shapes based trajectory queries for moving objects,” in Proceedings of the 13th annual ACM international workshop on Geographic information systems . ACM, 2005, pp. 21–30.
3. F. Hausdorff, “Grundz uge der mengenlehre,” 1914
4. M. M. Fr echet, “Sur quelques points du calcul fonctionnel,” Rendiconti del Circolo Matematico di Palermo (1884-1940) , vol. 22, no. 1, pp. 1–72, 1906.
5. H. Alt and M. Godau, “Computing the frechet distance between two polygonal curves,” International Journal of Computational Geometry & Applications , vol. 5, no. 01n02, pp. 75–91, 1995.
6. D. J. Berndt and J. Clifford , “Using dynamic time warping to find patterns in time series.” in KDD workshop, vol. 10, no. 16. Seattle, WA, 1994, pp. 359–370
7. M. Vlachos, G. Kollios, and D. Gunopulos, “Discovering similar multi- dimensional trajectories,” in Data Engineering, 2002. Proceedings. 18th International Conference on .IEEE, 2002, pp. 673–684
8. L. Chen and R. Ng, “On the marriage of lp-norms and edit distance,” in Proceedings of the Thirtieth international conference on Very large data bases-Volume 30 . VLDB Endowment, 2004, pp. 792–803.
9. L. Chen, M. T. ̈ Ozsu, and V. Oria, “Robust and fast similarity search for moving object trajectories,” in Proceedings of the 2005 ACM SIGMOD international conference on Management of data . ACM, 2005, pp. 491–502.
10. J.-G. Lee, J. Han, and K.-Y. Whang, “Trajectory clustering: a partition- and-group framework,” in Proceedings of the 2007 ACM SIGMOD international conference on Management of data . ACM, 2007, pp. 593–604.