Abstract
The paper illustrates basic methods of mobility data mining, designed to extract from the big mobility data the patterns of collective movement behavior, i.e., discover the subgroups of travelers characterized by a common purpose, profiles of individual movement activity, i.e., characterize the routine mobility of each traveler. We illustrate a number of concrete case studies where mobility data mining is put at work to create powerful analytical services for policy makers, businesses, public administrations, and individual citizens.
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Notes
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An high resolution version of the graphics is available online at http://kdd.isti.cnr.it/uma.
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Giannotti, F., Gabrielli, L., Pedreschi, D., Rinzivillo, S. (2016). Understanding Human Mobility with Big Data. In: Michaelis, S., Piatkowski, N., Stolpe, M. (eds) Solving Large Scale Learning Tasks. Challenges and Algorithms. Lecture Notes in Computer Science(), vol 9580. Springer, Cham. https://doi.org/10.1007/978-3-319-41706-6_10
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DOI: https://doi.org/10.1007/978-3-319-41706-6_10
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