Efficiently Retrieving Top-k Trajectories by Locations via Traveling Time

Yuxing Han, Lijun Chang, Wenjie Zhang, Xuemin Lin, Liping Wang

Abstract: The flourishing industry of location-based services has collected a massive amount of users’ positions in the form of spatial trajectories, which raise many research problems. In this paper, we study a trajectory retrieving query, k-TLT, which aims at retrieving the top-k Trajectories by Locations and ranked by traveling Time. Given a set Q of query locations, a k-TLT query retrieves top-k trajectories that are close to Q with respect to traveling time. In contrast to existing works which consider only location information, k-TLT queries also consider the traveling time information, which have many applications, such as travel route planning and moving object study. To efficiently answer a k-TLT query, we first online compute a list L q of trajectories for each query location q ∈ Q, such that trajectories in L q are ranked by their traveling time to q. Based on the online generated lists L q corresponding to query locations, a small set of candidate trajectories that are close to Q is selected by iteratively retrieving trajectories from lists L q . Then, the set of candidate trajectories is refined and pruned to determine the top-k trajectories. We conduct extensive experiments on a real trajectory dataset and verify the efficiency of our approach.