Abstract

The increasing availability of cutting-edge location-acquisition technologies such as GPS devices, has led to the generation of huge datasets of spatial trajectories. These trajectories store important information regarding the movement of people, vehicles, robots, animals, users of social networks, etc. Many research initiatives have applied data mining techniques in order to extract useful knowledge from this data.  An important, and yet complicated, pre-processing step in mining patterns from trajectory data, is the identification of the Regions of Interest (RoI) that have been collectively navigated by a set of trajectories. The RoI’s are being manually and subjectively pre-defined by a group of experts as popular regions, regardless of the actual behaviour of the moving objects. This research emphasizes the usefulness of applying an unsupervised machine learning technique, namely Self Organizing Map (SOM), in order to identify the RoI’s associated with a trajectory dataset depending on the moving objects’ behaviour. The research experiments were conducted using 180 thousand of the trajectories generated by 442 taxis running in the city of Porto, in Portugal, and they demonstrate the ability of SOM in identifying the RoI’s and interesting taxi trips within the city.

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