GPS-MTM: Capturing Pattern of Normalcy in GPS-Trajectories with self-supervised learning

• URL: http://arxiv.org/abs/2509.24031v2
• Date: Wed, 08 Oct 2025 08:21:22 GMT
• Title: GPS-MTM: Capturing Pattern of Normalcy in GPS-Trajectories with self-supervised learning
• Authors: Umang Garg, Bowen Zhang, Anantajit Subrahmanya, Chandrakanth Gudavalli, BS Manjunath,
• Abstract summary: We introduce the GPSMasked Trajectory Transformer (GPS-MTM), a foundation model for large-scale mobility data.<n>GPS-MTM decomposes mobility into two complementary modalities: states (point-of-interest categories) and actions (agent transitions)
• Score: 7.240185261197756
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Foundation models have driven remarkable progress in text, vision, and video understanding, and are now poised to unlock similar breakthroughs in trajectory modeling. We introduce the GPSMasked Trajectory Transformer (GPS-MTM), a foundation model for large-scale mobility data that captures patterns of normalcy in human movement. Unlike prior approaches that flatten trajectories into coordinate streams, GPS-MTM decomposes mobility into two complementary modalities: states (point-of-interest categories) and actions (agent transitions). Leveraging a bi-directional Transformer with a self-supervised masked modeling objective, the model reconstructs missing segments across modalities, enabling it to learn rich semantic correlations without manual labels. Across benchmark datasets, including Numosim-LA, Urban Anomalies, and Geolife, GPS-MTM consistently outperforms on downstream tasks such as trajectory infilling and next-stop prediction. Its advantages are most pronounced in dynamic tasks (inverse and forward dynamics), where contextual reasoning is critical. These results establish GPS-MTM as a robust foundation model for trajectory analytics, positioning mobility data as a first-class modality for large-scale representation learning. Code is released for further reference.

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