Participant Mobility Modeling and Prediction Sample Clauses

Participant Mobility Modeling and Prediction. Location is one of the most commonly used attribute in present databases. From a simple zipcode in U.S. Census data to GPS coordinates in navigation applications such as Google Maps, location data is collected everyday providing a rich source of information for a broad range of applications in transportation, epidemiology and health-care, economic development, and marketing. An emerging family of such applications exploit the predictive property of human mobility. Location-based re- minders/planners, cellular-user tracking [9], traffic prediction, epidemic prevention, and event prediction [114] are few examples of such applications. In Chapter 6, we present an extensive empirical evaluation of location prediction methods. To this end, we classify and review next location prediction approaches according to four different aspects: personalization, temporal representation, spatial representation, and mobility learning scheme.
Sample 1
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