Efficient Learning-based Top-k Representative Similar Subtrajectory Query

The advancement in location technology and the increase in trajectory data have made trajectory data mining a focal point in the field of spatiotemporal data analysis. Unlike the extensive research focused on the similarity of entire trajectories, this paper delves into subtrajectory similarity within collections containing a large number of trajectories, under a specified trajectory similarity metric. We propose the Top-k Representative Similar Subtrajectory Query problem, with the objective of identifying the top-k representative subtrajectories which are most similar to the query trajectory within a large trajectory set. To ensure diversity in the results, we adopt the concept of representative similarity, where only the subtrajectory with the highest similarity score to the query trajectory is reported, thus avoiding the redundancy in the top-$k$results. To address the challenge of high computational costs, we propose a learning-based framework, leveraging a deep learning model called Representative Similarity Score Estimation (RSSE) to approximate subtrajectory similarity scores efficiently and reduce the candidate set significantly. Empirical evaluations conducted on various real-world datasets substantiate the effectiveness and efficiency of our proposed method.