A survey on trajectory representation learning methods

Abstract

With the rapid development of Global Positioning System (GPS), Global System for Mobile Communications (GSM), and the widespread application of mobile devices, a massive amount of trajectory data have been generated. Current trajectory data processing methods typically require input in the form of fixed-length vectors, making it crucial to convert variable-length trajectory data into fixed-length, low-dimensional embedding vectors. Trajectory representation learning aims to transform trajectory data into more expressive and interpretable representations. This paper provides a comprehensive review of the research progress, methodologies, and applications of trajectory representation learning. First, it categorizes and introduces the key techniques of trajectory representation learning and summarizes the available public trajectory datasets. Then, it classifies trajectory representation learning methods based on various downstream tasks, with a focus on their principles, advantages, limitations, and application scenarios in trajectory similarity computation, similar trajectory search, trajectory clustering, and trajectory prediction. Additionally, representative model structures and principles in each task are analyzed, along with the characteristics and advantages of different methods in each task. Last, the challenges faced by current trajectory representation learning methods are analyzed, including data sparsity, multimodality, model optimization, and privacy protection, while potential research directions and methodologies to address these challenges are explored.

Graphical abstract

Keywords

trajectory representation learning / trajectory data mining / trajectory similarity computation / similar trajectory search / trajectory clustering / trajectory prediction

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