Mining GPS data to augment road models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining GPS Traces for Map Refinement
Data Mining and Knowledge Discovery
Adaptive learning of semantic locations and routes
Proceedings of the 1st international conference on Autonomic computing and communication systems
Detecting road intersections from GPS traces
GIScience'10 Proceedings of the 6th international conference on Geographic information science
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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.