Inferring human activities from GPS tracks
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
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The availability of inexpensive tracking devices, such as GPS-enabled devices, gives the opportunity to collect large amounts of trajectory data from vehicles. In this context, we are interested in the problem of generating the traffic information in time-dependent networks using this kind of data. This problem is not trivial since several works in literature use strong assumptions on the error distribution we want to drop, proposing a gravitational model method to compute road segment average speed from trajectory data. Furthermore we show how to generate travel-time functions from the computed average speeds useful for time-dependent networks routing systems. Our approach allows creating an accurate picture of the traffic conditions in time and space. The method we present in this paper tackles all this aspect showing how its performance over a synthetic dataset and a real case.