Trip Oriented Search on Activity Trajectory

Driven by the flourish of location-based services, trajectory search has received significant attentions in recent years. Different from existing studies that focus on searching trajectories with spatio-temporal information and text descriptions, we study a novel problem of searching trajectories with spatial distance, activities and rating scores. Given a query q with a threshold of distance, a set of activities, a start point S and a destination E, trip oriented search on activity trajectory (TOSAT) returns k trajectories that can cover the activities with the highest rating scores within the threshold of distance. In addition, we extend the query with an order, i.e., order-sensitive trip oriented search on activity trajectory (OTOSAT), which takes both the order of activities in a query q and the order of trajectories into consideration. It is very challenging to answer TOSAT and OTOSAT efficiently due to the structural complexity of trajectory data with rating information. In order to tackle the problem efficiently, we develop a hybrid index AC-tree to organize trajectories. Moreover, the optimized variant RAC⁺-tree and novel algorithms are introduced with the goal of achieving higher performance. Extensive experiments based on real trajectory datasets demonstrate that the proposed index structures and algorithms are capable of achieving high efficiency and scalability.