Mining GPS traces to recommend common meeting points

Abstract

Scheduling a meeting is a difficult task for people who have overbooked calendars and many constraints. The complexity increases when the meeting is to be scheduled between parties who are situated in geographically distant locations of a city and have varying travel patterns. In this paper, we present a solution that identifies a common meeting point for a group of users who have temporal and spatial locality constraints that vary over time. The problem entails answering an Optimal Meeting Point (OMP) query in spatial databases. Under Euclidean space OMP query solution identification gets reduced to the problem of determining the geometric median of a set of points, a problem for which no exact solution exists. The OMP problem does not consider any constraints as far as availability of users is concerned whereas that is a key constraint in our setting. We therefore focus on finding a solution that uses daily movements information obtained from GPS traces for each user to compute stay points during various times of the day. We then determine interesting locations by analyzing the stay points across multiple users. The novelty of our solution is that the computations are done within the database by using various relational algebra operations in combination with statistical operations on the GPS trajectory data. This makes our solution scalable to larger groups of users and for multiple such requests. Once this list of stay points and interesting locations are obtained, we show that this data can be utilized to construct spatio-temporal graphs for the users that allow us efficiently decide a meeting place. We perform experiments on a real-world dataset and show that our method is effective in finding an optimal meeting point between two users.