通过轨迹图像匹配，精准识别两人在时空中的共同移动行为。
Pairwise Spatiotemporal Partial Trajectory Matching for Co-movement Analysis
- 将时空数据转为可解释的轨迹图像，支持局部轨迹匹配。
- 在共走任务中达F1-score 0.73，优于现有方法。
- 适合研究社交行为、城市规划等需要分析共享模式的场景。
时空成对移动分析旨在识别个体在特定时间窗口内的共享地理行为。传统方法依赖序列建模与行为分析技术处理表格或视频数据，但常缺乏可解释性且难以捕捉部分匹配。本文提出一种新型成对时空部分轨迹匹配方法，将表格型时空数据转换为指定时间窗下的可解释轨迹图像，实现轨迹定位、空间重叠检测及基于孪生神经网络的成对匹配。我们在共走分类任务上评估该方法，证明其在新提出的共行为识别应用中的有效性，模型最高获得0.73的F1-score。此外，我们探索了该方法在真实场景中成对日常行为模式分析的潜力，揭示了共享行为的频率、时间和持续时长等特征。该框架为时空行为分析提供了强大且可解释的解决方案，适用于社会行为研究、城市规划与医疗健康等领域。
原文摘要 · Abstract (English)
Spatiotemporal pairwise movement analysis involves identifying shared geographic-based behaviors between individuals within specific time frames. Traditionally, this task relies on sequence modeling and behavior analysis techniques applied to tabular or video-based data, but these methods often lack interpretability and struggle to capture partial matching. In this paper, we propose a novel method for pairwise spatiotemporal partial trajectory matching that transforms tabular spatiotemporal data into interpretable trajectory images based on specified time windows, allowing for partial trajectory analysis. This approach includes localization of trajectories, checking for spatial overlap, and pairwise matching using a Siamese Neural Network. We evaluate our method on a co-walking classification task, demonstrating its effectiveness in a novel co-behavior identification application. Our model surpasses established methods, achieving an F1-score up to 0.73. Additionally, we explore the method's utility for pair routine pattern analysis in real-world scenarios, providing insights into the frequency, timing, and duration of shared behaviors. This approach offers a powerful, interpretable framework for spatiotemporal behavior analysis, with potential applications in social behavior research, urban planning, and healthcare.
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