A New Similarity Measure Between Semantic Trajectories Based on Road Networks

With the development of the positioning technology, studies on trajectories have been growing rapidly in the past decades. As a fundamental part involved in trajectory recommendation and prediction, trajectory similarity has attracted considerable attention from researchers. However, most existing works focus on raw trajectory similarity by comparing their shapes, while very few works study semantic trajectory similarity and none of them take all the geographical, semantic, and timestamp information into consideration. In this paper, we model semantic trajectories based on road networks considering all these information, and propose a Constrained Time-based Common Parts (CTCP) approach to measure the similarity. Since the strict time constraint in CTCP may lead to many zero values, we further propose an improved Weighted Constrained Time-based Common Parts (WCTCP) method by relaxing the time constraint to measure the similarity more accurately. We conducted extensive performance studies on real datasets to confirm the effectiveness of our approaches.