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Toward an accurate mobility trajectory recovery using contrastive learning

基于对比学习的移动轨迹准确恢复

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Abstract

Human mobility trajectories are fundamental resources for analyzing mobile behaviors in urban computing applications. However, these trajectories, typically collected from location-based services, often suffer from sparsity and irregularity in time. To support the development of mobile applications, there is a need to recover or estimate missing locations of unobserved time slots in these trajectories at a fine-grained spatial-temporal resolution. Existing methods for trajectory recovery rely on either individual user trajectories or collective mobility patterns from all users. The potential to combine individual and collective patterns for precise trajectory recovery remains unexplored. Additionally, current methods are sensitive to the heterogeneous temporal distributions of the observable trajectory segments. In this paper, we propose CLMove (where CL stands for contrastive learning), a novel model designed to capture multilevel mobility patterns and enhance robustness in trajectory recovery. CLMove features a two-stage location encoder that captures collective and individual mobility patterns. The graph neural network based networks in CLMove explore location transition patterns within a single trajectory and across various user trajectories. We also design a trajectory-level contrastive learning task to improve the robustness of the model. Extensive experimental results on three representative real-world datasets demonstrate that our CLMove model consistently outperforms state-of-the-art methods in terms of trajectory recovery accuracy.

摘要

在城市计算应用中, 用户轨迹数据是用户移动行为分析的基础数据源. 然而, 由于这些用户轨迹数据部分是从基于位置的服务中收集的, 在时间上常常具有稀疏性和不规则性. 为提高基于位置数据服务的性能, 以较高时空分辨率对用户轨迹数据进行恢复, 对无记录时刻的用户地点进行预测是非常重要的. 本文提出一个新的轨迹恢复模型, 旨在捕捉多级移动模式并增强轨迹恢复的稳健性. 该模型具有一个两阶段位置编码器, 用于捕捉集体和个体移动模式, 并利用基于图神经网络的网络与注意力机制捕捉单个轨迹内部和跨多个用户轨迹的位置转移模式. 此外, 采用一个轨迹级对比学习任务以提高模型的稳健性. 在3个具有代表性的真实数据集上的大量实验结果表明, 该模型在轨迹恢复精度方面始终具有优越的性能.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Authors

Contributions

Yushan LIU and Yang CHEN designed the research. Yushan LIU processed the data and drafted the paper. Yang CHEN and Jiayun ZHANG helped organize the paper. Yu XIAO and Xin WANG revised and finalized the paper.

Corresponding author

Correspondence to Yang Chen  (陈阳).

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All the authors declare that they have no conflict of interest.

Additional information

Project supported by the National Natural Science Foundation of China (Nos. 62072115 and 61971145) and the Shanghai Science and Technology Innovation Action Plan Project, China (No. 22510713600)

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Liu, Y., Chen, Y., Zhang, J. et al. Toward an accurate mobility trajectory recovery using contrastive learning. Front Inform Technol Electron Eng 25, 1479–1496 (2024). https://doi.org/10.1631/FITEE.2300647

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  • DOI: https://doi.org/10.1631/FITEE.2300647

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