Document - Mining mobility user profiles for car pooling

2011

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Mining mobility user profiles for car pooling

Trasarti Roberto, Pinelli Fabio, Nanni Mirco, Giannotti Fosca

spatio-temporal data mining mobility application trajectory patter

In this paper we introduce a methodology for extracting mobility profiles of individuals from raw digital traces (in particular, GPS traces), and study criteria to match individuals based on profiles. We instantiate the profile matching problem to a specific application context, namely proactive car pooling services, and therefore develop a matching criterion that satisfies various basic constraints obtained from the background knowledge of the application domain. In order to evaluate the impact and robustness of the methods introduced, two experiments are reported, which were performed on a massive dataset containing GPS traces of private cars: (i) the impact of the car pooling application based on profile matching is measured, in terms of percentage shareable traffic; (ii) the approach is adapted to coarser-grained mobility data sources that are nowadays commonly available from telecom operators. In addition the ensuing loss in precision and coverage of profile matches is measured.

Source: 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11. ACM Press : New York (Stati Uniti d'America), pp. 1190–1198, San Diego, CA, USA, 21-08 2011

Publisher: ACM Press, New York, USA

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BibTeX entry

@inproceedings{oai:it.cnr:prodotti:206348,
	title = {Mining mobility user profiles for car pooling},
	author = {Trasarti Roberto and Pinelli Fabio and Nanni Mirco and Giannotti Fosca},
	publisher = {ACM Press, New York, USA},
	doi = {10.1145/2020408.2020591},
	booktitle = {17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11. ACM Press :  New York (Stati Uniti d'America), pp. 1190–1198, San Diego, CA, USA, 21-08 2011},
	year = {2011}
}