Fast Heuristic Algorithm for Similarity of Trajectories Using Discrete Fréchet Distance Measure

Title & Authors
Fast Heuristic Algorithm for Similarity of Trajectories Using Discrete Fréchet Distance Measure
Park, Jinkwan; Kim, Taeyong; Park, Bokuk; Cho, Hwan-Gue;

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 $\small{Fr{\acute{e}}chet}$ distance measure is used to calculate the distance between a pair of trajectories. However, the $\small{Fr{\acute{e}}chet}$ 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 $\small{Fr{\acute{e}}chet}$ 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 $\small{Fr{\acute{e}}chet}$ distance.
Keywords
vehicle GPS data;trajectory distance;$\small{Fr{\acute{e}}chet}$ distance;similarity of trajectory;
Language
Korean
Cited by
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