Survey on Trajectory Representation Learning Methods

doi:10.3778/j.issn.1673-9418.2409019

Abstract

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 has 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. Firstly, it categorizes and introduces the key techniques of trajectory representation learning and summarizes the available public trajectory datasets. And 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. Lastly, the challenges faced by current trajectory representation learning methods are analyzed, including data sparsity, multimodality, model optimization, privacy protection, etc., while potential research directions and methodologies to address these challenges are explored.

Key words: trajectory representation learning, trajectory data mining, trajectory similarity computation, similar trajectory search, trajectory clustering, trajectory prediction

摘要： 全球定位系统（GPS）、全球移动通信系统（GSM）的快速发展以及移动设备的普遍应用，产生了大量的轨迹数据。目前的轨迹数据处理方法通常以定长的向量形式输入到模型，因此如何将变长的轨迹数据转换成定长低维的嵌入向量十分重要。轨迹表示学习旨在将轨迹数据转换为更具表达力和可解释性的表示形式。对轨迹表示学习的研究现状、方法及应用进行了全面综述。分类介绍了轨迹表示学习的关键技术，总结了现有轨迹公开数据集。将轨迹表示学习方法按照不同的下游任务进行分类，重点综述了轨迹表示学习方法在轨迹相似性计算、相似轨迹搜索、轨迹聚类、轨迹预测等领域的原理、优缺点和应用，并分别分析了每一类任务中具有代表性的模型结构和原理，及各类任务中不同方法的特点和优势。分析了当前轨迹表示学习所面临的挑战，探讨了如何解决轨迹表示学习中的数据稀疏性、多模态以及模型优化与隐私保护等问题，并提出了具体的研究思路和方法。

关键词: 轨迹表示学习, 轨迹数据挖掘, 轨迹相似性计算, 相似轨迹搜索, 轨迹聚类, 轨迹预测

MENG Xiangfu, SUN Shuonan, ZHANG Xiaoyan, LENG Qiangkui, FANG Jinfeng. Survey on Trajectory Representation Learning Methods[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(6): 1437-1454.

孟祥福, 孙硕男, 张霄雁, 冷强奎, 方金凤. 轨迹表示学习方法研究综述[J]. 计算机科学与探索, 2025, 19(6): 1437-1454.

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