Measuring the Distance of Moving Objects from Big Trajectory Data

DOI

10.2991/ijndc.2017.5.2.6How to use a DOI?

Keywords

Big Trajectory Data, Moving Objects, Geographic Distance, Semantic Similarity.

Abstract

Location-based services have become important in social networking, mobile applications, advertising, traffic monitoring, and many other domains. The growth of location sensing devices has led to the vast generation of dynamic spatial-temporal data in the form of moving object trajectories which can be characterized as big trajectory data. Big trajectory data enables the opportunities such as analyzing the groups of moving objects. To obtain such facilities, the issue of this work is to find a distance measurement method that respects the geographic distance and the semantic similarity for each trajectory. Measurement of similarity between moving objects is a difficult task because not only their position changes but also their semantic features vary. In this research, a method to measure trajectory similarity based on both geographical features and semantic features of motion is proposed. Finally, the proposed methods are practically evaluated by using real trajectory dataset.

Copyright

© 2017, 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  - JOUR
AU  - Khaing Phyo Wai
AU  - Nwe Nwe
PY  - 2017
DA  - 2017/04/03
TI  - Measuring the Distance of Moving Objects from Big Trajectory Data
JO  - International Journal of Networked and Distributed Computing
SP  - 113
EP  - 122
VL  - 5
IS  - 2
SN  - 2211-7946
UR  - https://doi.org/10.2991/ijndc.2017.5.2.6
DO  - 10.2991/ijndc.2017.5.2.6
ID  - Wai2017
ER  -