Efficient kNN query for moving objects on time-dependent road networks

In this paper, we study the Time-Dependent k Nearest Neighbor (TD- k NN) query on moving objects that aims to return k objects arriving at the query location with the least traveling cost departing at a given time t . Although the k NN query on moving objects has been widely studied in the scenario of the static road network, the TD- k NN query tends to be more complicated and challenging because under the time-dependent road network, the cost of each edge is measured by a cost function rather than a fixed distance value. To tackle such difficulty, we adopt the framework of GLAD and develop an advanced index structure to support efficient fastest travel cost query on time-dependent road network. In particular, we propose the Time-Dependent H2H (TD-H2H) index, which pre-computes the aggregated weight functions between each node to some specific nodes in the decomposition tree derived from the road network. Additionally, we establish a grid index on moving objects for candidate object retrieval and location update. To further accelerate the TD- k NN query, two pruning strategies are proposed in our solution. Apart from that, we extend our framework to tackle the time-dependent approachable k NN (TD-A k NN) query on moving objects targeting for the application of taxi-hailing service, where the moving object might have been occupied. Extensive experiments with different parameter settings on real-world road network show that our solutions for both TD- k NN and TD-A k NN queries are superior to the competitors in orders of magnitude.