An integrated space-time pattern classification approach for individuals' travel trajectories

Authors:
Zhixiang Fang;Qingquan Li
Affiliations:
The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, P.R.China;The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, P.R.China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Year:
2009

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Abstract

Recently, one significant challenge for scientists is how to mine useful patterns of moving objects' trajectories, with the increasing individual data collected by location-aware technologies. This paper proposes an integrated space-time pattern classification approach for individuals' travel trajectories, which differentiates itself from traditional data mining techniques, such as clustering, frequent pattern discovery and so on. This approach can classify these trajectories by virtue of taking movement's direction, distance, and time into account, and has the advantages over traditional data mining techniques in the aspect of space-time pattern mining. The experimental results has demonstrated its ability of supporting space-time pattern analysis and its capability of classification for a huge amount of trajectories.