Distributional Kernel: An Effective and Efficient Means for Trajectory Retrieval

In this paper, we propose a new and powerful way to represent trajectories and measure the distance between them using a distributional kernel. Our method has two unique properties: (i) the identity property which ensures that dissimilar trajectories have no short distances, and (ii) a runtime orders of magnitude faster than that of existing distance measures. An extensive evaluation on several large real-world trajectory datasets confirms that our method is more effective and efficient in trajectory retrieval tasks than traditional and deep learning-based distance measures.