Extracting Visited Points of Interest from Vehicle Trajectories

Identifying visited points of interest (PoIs) from vehicle trajectories remains an open problem that is difficult due to vehicles parking often at some distance from the visited PoI and due to some regions having a high PoI density. We propose a visited PoI extraction (VPE) method that identifies visited PoIs using a Bayesian network. The method considers stay duration, weekday, arrival time, and PoI category to compute the probability that a PoI is visited. We also provide a method to generate labeled data from unlabeled GPS trajectories. An experimental evaluation shows that VPE achieves a precision@3 value of 0.8, indicating that VPE is able to model the relationship between the temporal features of a stop and the category of the visited PoI.