JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Fast Heuristic Algorithm for Similarity of Trajectories Using Discrete Fréchet Distance Measure
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Fast Heuristic Algorithm for Similarity of Trajectories Using Discrete Fréchet Distance Measure
Park, Jinkwan; Kim, Taeyong; Park, Bokuk; Cho, Hwan-Gue;
  PDF(new window)
 Abstract
A trajectory is the motion path of a moving object. The advances in IT have made it possible to collect an immeasurable amount of various type of trajectory data from a moving object using location detection devices like GPS. The trajectories of moving objects are widely used in many different fields of research, including the geographic information system (GIS) field. In the GIS field, several attempts have been made to automatically generate digital maps of roads by using the vehicle trajectory data. To achieve this goal, the method to cluster the trajectories on the same road is needed. Usually, the distance measure is used to calculate the distance between a pair of trajectories. However, the distance measure requires prolonged calculation time for a large amount of trajectories. In this paper, we presented a fast heuristic algorithm to distinguish whether the trajectories are in close distance or not using the discrete distance measure. This algorithm trades the accuracy of the resulting distance with decreased calculation time. By experiments, we showed that the algorithm could distinguish between the trajectory within 10 meters and the distant trajectory with 95% accuracy and, at worst, 65% of calculation reduction, as compared with the discrete distance.
 Keywords
vehicle GPS data;trajectory distance; distance;similarity of trajectory;
 Language
Korean
 Cited by
 References
1.
C. Lili and K. John, "From GPS traces to a routable road map," Proc. of 17th ACM SIGSPATIAL, pp. 3-12, 2009.

2.
W. Rodriguez, et aI., "3-dimensional curve similarity using string matching," J. of Robotics and Autonomous Systems, Vol. 49, 3, pp. 165-172, 2004. crossref(new window)

3.
B. Liao, Y. Zhang, K. Ding, and T.-m. Wang, "Analysis of similarity/dissimilarity of dna sequences based on a condensed curve representation," J. of Molecular Structure: THEOCHEM, Vol. 717, pp. 199-203, 2005. crossref(new window)

4.
A. Efrat, Q. Fan, and S. Venkatasubramanian, "Curve matching, time warping, and light fields : New algorithms for computing similarity between curves," J. of Mathematical Imaging and Vision, Vol. 27, pp. 203- 216, 2007. crossref(new window)

5.
Alt H, Godau M., "Computing the Frechet distance between two polygonal curves," International Journal of Computational Geometry & Applications, Vol. 5, 01n02, pp. 75-91, 1995. crossref(new window)

6.
Eiter T., Mannila H., "Computing discrete Frechet distance," Citeseer, 1994.

7.
A. Mosig and M. Clausen, "Approximately matching polygonal curves with respect to the Frechet distance," J. of Computational Geometry, Vol. 30, pp. 113-127, 2005. crossref(new window)

8.
P. K. Agarwal, R. B. Avraham, H. Kaplan and M . Sharir, "Computing the Discrete Frechet Distance in Subquadratic Time," SIAM Journal on Computing, Vol. 43, No. 2, pp. 429-449, 2014. crossref(new window)

9.
R. B. Avraham, O. Filtser, H. Kaplan and M. J. Katz, "The Discrete and Semi-continuous Frechet Distance with Shortcuts via Approximate Distance Counting and Selection," Proc. of 30th SOCG, pp. 377-386, 2014.

10.
A. Driemel and S. Har-Peled, "Jaywalking your Dog-Computing the Frechet Distance with Shortcuts," Proc. of 23th ACM-SIAM Symp. on Discrete Algorithms, pp. 318-337, 2012.

11.
K. Bringmann, "Why walking the dog takes time: Frechet distance has no strongly subquadratic algorithms unless SETH fails," FOCS, 2014 IEEE 55th Annual Symposium on, pp. 661-670, 2014.