NDLI: Where you go and how you go: Mining to predict destination and future route

Content Provider	IEEE Xplore Digital Library
Author	Mingqi Lv Chen, Ling Chen, Gencai
Copyright Year	2010
Description	Author affiliation: College of Computer Science, Zhejiang University, Hangzhou 310027, China (Mingqi Lv; Chen, Ling; Chen, Gencai)
Abstract	The ability to predict the future movement behavior of a person can help to improve many pervasive applications that exploit the locations of their users. In this paper, we propose an approach to predict both the intended destination and the future route of a person. Rather than predicting the destination and future route separately, we have focused on making prediction in an integrated way by exploiting personal movement data collected by GPS. Since personal movement data is more informative than the movement data of vehicles, the proposed approach first detects the significant places of a person using a clustering-based algorithm called FBM, and then extracts movement patterns using an extended CRPM algorithm. Extracted movement patterns are organized as couples of places where a person departs from and goes to. The prediction is made based on a pattern tree built from these movement patterns. Finally, with the real personal movement data of 14 participants, we conducted a number of experiments to evaluate the performance of our system. The results show that our approach can achieve approximately 80% and 60% accuracy in destination prediction and 1-step prediction, respectively.
Starting Page	171
Ending Page	179
File Size	1522474
Page Count	9
File Format	PDF
ISBN	9781424453290
e-ISBN	9781424453283
DOI	10.1109/PERCOM.2010.5466979
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-29
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Trajectory route prediction context-aware computing location data mining route pattern
Content Type	Text
Resource Type	Article

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