计算机科学与探索2025，Vol.19Issue(6)：1437-1454,18.DOI:10.3778/j.issn.1673-9418.2409019
轨迹表示学习方法研究综述
Survey on Trajectory Representation Learning Methods
摘要
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.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Key words
trajectory representation learning/trajectory data mining/trajectory similarity computation/similar trajectory search/trajectory clustering/trajectory prediction分类
计算机与自动化引用本文复制引用
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):1437-1454,18.基金项目
国家自然科学基金(61772249). This work was supported by the National Natural Science Foundation of China(61772249). （61772249）
