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This paper presents a recommender system for both taxi drivers and people expecting to take a taxi, using the knowledge of 1) passengers' mobility patterns and 2) taxi drivers' picking-up/dropping-off behaviors learned from the GPS trajectories of taxicabs. First, this recommender system provides taxi drivers with some locations and the routes to these locations, toward which they are more likely to pick up passengers quickly (during the routes or in these locations) and maximize the profit of the next trip. Second, it recommends people with some locations (within a walking distance) where they can easily find vacant taxis. In our method, we learn the above-mentioned knowledge (represented by probabilities) from GPS trajectories of taxis. Then, we feed the knowledge into a probabilistic model that estimates the profit of the candidate locations for a particular driver based on where and when the driver requests the recommendation. We build our system using historical trajectories generated by over 12,000 taxis during 110 days and validate the system with extensive evaluations including in-the-field user studies.

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The advances in location positioning and wireless communication technologies have led to a myriad of spatial trajectories representing the mobility of a variety of moving objects. While processing trajectory data with the focus of spatio-temporal features has been widely studied in the last decade, recent proliferation in location-based web applications (e.g., Foursquare, Facebook) has given rise to large amounts of trajectories associated with activity information, called activity trajectory. In this paper, we study the problem of efficient similarity search on activity trajectory database. Given a sequence of query locations, each associated with a set of desired activities, an activity trajectory similarity query (ATSQ) returns k trajectories that cover the query activities and yield the shortest minimum match distance. An order-sensitive activity trajectory similarity query (OATSQ) is also proposed to take into account the order of the query locations. To process the queries efficiently, we firstly develop a novel hybrid grid index, GAT, to organize the trajectory segments and activities hierarchically, which enables us to prune the search space by location proximity and activity containment simultaneously. In addition, we propose algorithms for efficient computation of the minimum match distance and minimum order-sensitive match distance, respectively. The results of our extensive empirical studies based on real online check-in datasets demonstrate that our proposed index and methods are capable of achieving superior performance and good scalability.

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