A Novel Algorithm for Detecting Spatial-Temporal Trajectory Outlier

DOI

10.2991/cset-16.2016.44How to use a DOI?

Keywords

Spatial-temporal trajectory, outlier detection, parallel data mining

Abstract

As an important area of spatial-temporal data mining, trajectory outlier detection has already attracted broad attention in recent years. In this paper, we present a novel distance measurement between spatial-temporal sub-trajectories and propose algorithm STOD for detecting outliers in both spatial and temporal dimensions jointly. Each trajectory is divided into line segments at first, and the corresponding minimal boundary boxes are constructed. After combination, the outlier index is computed with our distance measurements including overlapping volume, angle and speed. To improve the efficiency of algorithm STOD, we present algorithm PSTOD for parallel detecting spatial-temporal trajectory outlier, which is implemented using Spark framework. The experiment results on real taxi dataset show that the two algorithms are effective and efficient.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shenglan Lv
AU  - Yifan Zhang
AU  - Genlin Ji
AU  - Bin Zhao
PY  - 2016/08
DA  - 2016/08
TI  - A Novel Algorithm for Detecting Spatial-Temporal Trajectory Outlier
BT  - Proceedings of the 2016 International Conference on Computer Science and Electronic Technology
PB  - Atlantis Press
SP  - 184
EP  - 190
SN  - 2352-538X
UR  - https://doi.org/10.2991/cset-16.2016.44
DO  - 10.2991/cset-16.2016.44
ID  - Lv2016/08
ER  -