Frequent trajectory mining on GPS data

Abstract

In this paper we propose a new algorithm for finding the frequent routes that a user has in his daily routine, in our method we build a grid in which we map each of the GPS data points that belong to a certain sequence. (We consider that each sequence conforms a route) we then carry out an interpolation procedure that has a probabilistic basis and find a more precise description of the user's trajectory. For each trajectory we find the edges that were crossed, with the crossed edges we create a histogram in which the bins denote the crossed edges and the frequency value the number of times that edge was crossed for a certain user. We then select the K most frequent edges and combine them to create a list of the most frequent paths that a user has. We compared our results with the algorithm that was proposed in Adaptive learning of semantic locations and routes [6] to find frequent routes of a user, and found that our implementation on the contrary of [6] can discriminate directions, ie routes that go from A to B and routes that go from B to A are taken as different. Furthermore our implementation also permits the analysis of subsections of the routes, something that to our knowledge had not been carried out in previous related work.