Exploring real mobility data with M-Atlas

Trasarti R., Rinzivillo S., Pinelli F., Nanni M., Monreale A., Renso C., Pedreschi D., Giannotti F.

Applications and ExpertSystems  Database Management. Data mining  Computational transportation science (CTS)  Data mining 

Research on moving-object data analysis has been recently fostered by the widespread diffusion of new techniques and systems for monitoring, collecting and storing loca- tion aware data, generated by a wealth of technological infrastructures, such as GPS positioning and wireless networks. These have made available massive repositories of spatio-temporal data recording human mobile activities, that call for suitable analytical methods, capable of enabling the development of innovative, location-aware applica- tions [3]. The M-Atlas is the evolution of the system presented in [5] allows to handle the whole knowledge discovery process from mobility data. The analysis capabilities of M-Atlas system have been applied onto a massive real life GPS dataset, obtained from 17,000 vehicles with on-board GPS receivers under a specific car insurance contract, tracked during one week of ordinary mobile activity in the urban area of the city of Milan; the dataset contains more than 2 million observations leading to a set of more than 200,000 trajectories.

Source: ECML PKDD 2010 - Machine Learning and Knowledge Discovery in Databases. European Conference, pp. 624–627, Barcelona, Spain, 20-24 September 2010

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