Survey on Deep Learning Based Trajectory Similarity Measurement Approaches

The development and application of mobile communication and sensing device technology have generated a large amount of trajectory data, which presents characteristics such as high-dimensional heterogeneity, multi-granularity, and uncertainty, which makes traditional trajectory similarity measurement methods based on point pair matching difficult to apply. In recent years, deep learning techniques have been applied to measuring trajectory similarity, aiming to mine more trajectory features, improve computational efficiency, and enhance model robustness. This paper systematically reviews recent trajectory similarity measurement methods based on deep learning. Firstly, it explains the relevant definitions of trajectories. Then, it provides an overview of these methods from two perspectives: metric representation forms (i.e. sequence representation and graph representation) and learning strategies (i.e. representation learning, metric learning, and contrastive learning). Furthermore, it conducts a detailed and comparative analysis on the implementation principles and characteristics of these methods from three aspects: trajectory data preprocessing, embedding representation learning, and similarity measurement. Afterwards, the commonly used datasets and evaluation metrics for trajectory similarity measurement methods based on deep learning are analyzed, and the sources, evaluation metrics, time complexity, and application scenarios of the learning model are summarized. Finally, it analyzes the challenges faced by trajectory similarity measurement methods and prospects future research directions.