The similarity is then used to identify a person associating him/her with the nearest mobility traces using Eq. 5. Note that we are working under the assumption that the sampled points all belong to a single mobility trace.
Finally, we introduce a set of real mobility traces that we used to test our definitions.
Secondly, we have evaluated the uniqueness of GPS mobility traces using three popular datasets.
We consider a total of eight mobility traces, with different vehicle densities.
With respect to the problem of identification of people from human mobility, Gruteser et al. [23] exploited the associativity of GPS mobility traces to identify individual traces from a collection of unlabeled traces of multiple users.
Using synthetic and real-world mobility traces, the proposed predictor does not only outperform existing mobility prediction algorithms but achieves accuracy scores higher by an order of magnitude.
In this paper we extracted approximately 790,000 mobility traces from a web-based repository of GPS tracks the Nokia Sports Tracker Service.
Each set contains ∼300,000 mobility traces for a two-week time span; a mobility trace contains the cell tower IDs and time stamps of calls and text messages made by one anonymized customer.
We turn CDR mobility traces to estimates on travel times between Senegalese cities, filling an existing gap in knowledge.
From the mobility traces of users, they classify places where the user is observed between: home, work, other meaningful, and elsewhere, the latter representing only pass-by places.
