Document - Mobility data mining: discovering movement patterns from trajectory data

2010

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Mobility data mining: discovering movement patterns from trajectory data

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

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

The analysis of movement data has been recently fostered by the widespread diffusion of new techniques and systems for monitoring, collecting and storing location-aware data, generated by a wealth of technological infrastructures, such as GPS positioning and wireless networks [2]. These have made available massive repositories of spatio-temporal data recording human mobile activities, such as location data from mobile phones, GPS tracks from mobile devices, etc.: is it possible to discover from these data use- ful and timely knowledge about human mobility? The GeoPKDD project [1], since 2005, investigated this direction of research; the lesson learned is that there is a long way to go from raw data of individual trajectories up to high-level collective mobility knowledge, capable of supporting the decisions of mobility and transportation managers. Such analysts reason about semantically rich concepts, such as systematic vs. occasional movement behavior and home- work commuting patterns; accordingly, the mainstream analytical tools of transportation engineering, such as origin/destination ma- trices, are based on semantically rich data collected by means of field surveys and interviews. Clearly, the price to pay for this rich- ness is hard: mass surveys are very expensive, so that their peri- odicity is very broad and obsolescence is rapid; poor data quality is also a plague: people tend to respond elusively and inaccurately. On the other extreme, automatically sensed mobility data record in- dividual trajectories at mass level, in real time. Clearly, the price topay here is exactly the lack of semantics in raw data: How to bridgeFigure 1: The steps of the mobility knowledge discovery pro- cess.

Source: International Workshop on Computational Transportation Science, pp. 7–10, San Jose, CA, USA, 3-5 November 2010

Publisher: ACM Press, New York, USA

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@inproceedings{oai:it.cnr:prodotti:92103,
	title = {Mobility data mining: discovering movement patterns from trajectory data},
	author = {Giannotti F. and Nanni M. and Pedreschi D. and Pinelli F. and Renso C. and Rinzivillo S. and Trasarti R.},
	publisher = {ACM Press, New York, USA},
	doi = {10.1145/1899441.1899444},
	booktitle = {International Workshop on Computational Transportation Science, pp. 7–10, San Jose, CA, USA, 3-5 November 2010},
	year = {2010}
}