Pairwise Spatiotemporal Partial Trajectory Matching for Co-movement Analysis

TL;DR

This paper introduces a novel interpretable method for pairwise spatiotemporal trajectory matching using trajectory images and Siamese Neural Networks, improving co-movement behavior analysis.

Contribution

It presents a new approach transforming spatiotemporal data into interpretable images for partial trajectory matching, enhancing analysis accuracy and interpretability.

Findings

Achieved up to 0.73 F1-score in co-walking classification

Outperformed existing methods in partial trajectory matching

Provided insights into shared behavior patterns in real-world scenarios

Abstract

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.