Agatha: Predicting Daily Activities from Place Visit History for Activity-Aware Mobile Services in Smart Cities

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We present a place-history-based activity prediction system called Agatha, in order to enable activity-aware mobile services in smart cities. The system predicts a user's potential subsequent activities that are highly likely to occur given a series of information about activities done before or activity-related contextual information such as visit place and time. To predict the activities, we develop a causality-based activity prediction model using Bayesian networks. The basic idea of the prediction is that where a person has been and what he/she has done so far influence what he/she will do next. To show the feasibility, we evaluate the prediction model using the American Time-Use Survey (ATUS) dataset, which includes more than 10,000 people's location and activity history. Our evaluation shows that Agatha can predict users' potential activities with up to 90% accuracy for the top 3 activities, more than 80% for the top 2 activities, and about 65% for the top 1 activity while considering a relatively large number of daily activities defined in the ATUS dataset, that is, 17 activities.
Publisher
HINDAWI PUBLISHING CORP
Issue Date
2015
Language
English
Article Type
Article
Citation

INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS

ISSN
1550-1329
DOI
10.1155/2015/867602
URI
http://hdl.handle.net/10203/205660
Appears in Collection
CS-Journal Papers(저널논문)
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