frequent trajectory mining on gps data
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.
gps tracking frequent user paths