With the popularity of positioning devices, Web 2.0 technology, and trip sharing services, many users are willing to log and share their trips on the Web. Thus, trip planning Web sites are able to provide some new services by inferring Regions-Of-Interest (ROIs) and recommending popular travel routes from trip trajectories. We argue that simply providing some travel routes consisting of popular ROIs to users is not sufficient. To tour around a wide geographical area, for example, a city, some users may prefer a trip to visit as many ROIs as possible, while others may like to stop by only a few ROIs for an in-depth visit. We refer to a trip fitting the former user group as an in-breadth trip and a trip suitable for the latter user group as an in-depth trip. Prior studies on trip planning have focused on mining ROIs and travel routes without considering these different preferences. In this article, given a spatial range and a user preference of depth/breadth specified by a user, we develop a Pattern-Aware Trajectory Search (PATS) framework to retrieve the top K trajectories passing through popular ROIs. PATS is novel because the returned travel trajectories, discovered from travel patterns hidden in trip trajectories, may represent the most valuable travel experiences of other travelers fitting the user's trip preference in terms of depth or breadth. The PATS framework comprises two components: travel behavior exploration and trajectory search. The travel behavior exploration component determines a set of ROIs along with their attractive scores by considering not only the popularity of the ROIs but also the travel sequential relationships among the ROIs. To capture the travel sequential relationships among ROIs and to derive their attractive scores, a user movement graph is constructed. For the trajectory search component of PATS, we formulate two trajectory score functions, the depth-trip score function and the breadth-trip score function, by taking into account the number of ROIs in a trajectory and their attractive scores. Accordingly, we propose an algorithm, namely, Bounded Trajectory Search (BTS), to efficiently retrieve the top K trajectories based on the two trajectory scores. The PATS framework is evaluated by experiments and user studies using a real dataset. The experimental results demonstrate the effectiveness and the efficiency of the proposed PATS framework.
|ACM Transactions on Intelligent Systems and Technology
|Published - Jun 2013
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Artificial Intelligence