Periodic Pattern Mining Based on GPS Trajectories

DOI

10.2991/isaeece-16.2016.36How to use a DOI?

Keywords

Mobile Data Mining; GPS Trajectories; Periods Detection

Abstract

With the rise of LBS (Location Based Service), lots of user recommendation system based on location trajectory analysis has emerged. Finding periodic behavior is essential to analysis user’s activity. In order to solve the problems with trajectory outliers and human intervention in periodic parameters, we propose a three-stage framework called PPM (Periodic Pattern Mining) to detect periodic pattern based on people's trajectory. First of all, we preprocess the trajectory data to extract stay points. Secondly, the sequence of stay points are clustered to construct the important places. At the last stage, the movement sequence is transformed into a binary sequence. Then period of every binary sequence is detected by a probabilistic model. The experiment based on public mobile dataset shows that the proposed method can be used to mine people’s periodic activity pattern effectively.

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  - Xiaopeng Chen
AU  - Dianxi Shi
AU  - Banghui Zhao
AU  - Fan Liu
PY  - 2016/04
DA  - 2016/04
TI  - Periodic Pattern Mining Based on GPS Trajectories
BT  - Proceedings of the 2016 International Symposium on Advances in Electrical, Electronics and Computer Engineering
PB  - Atlantis Press
SP  - 181
EP  - 187
SN  - 2352-5401
UR  - https://doi.org/10.2991/isaeece-16.2016.36
DO  - 10.2991/isaeece-16.2016.36
ID  - Chen2016/04
ER  -