BRIDGE: Bridge GPS and Road Trajectories via Dual Cross-Modal Masked Reconstruction

BRIDGE: Bridge GPS and Road Trajectories via Dual Cross-Modal Masked Reconstruction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article BRIDGE: Bridge GPS and Road Trajectories via Dual Cross-Modal Masked Reconstruction XueWei Li, Lai Wei, Yuehai Xu, Junting Li, Yaohua Sun, Xinyi Cheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7596741/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Trajectory Representation Learning (TRL) serves as a core technology supporting downstream tasks, such as mobility prediction and analysis. However, most existing TRL methods rely on single-modality modeling of global positioning system(GPS) trajectories or road trajectories, which limits their ability to simultaneously capture complementary information in geographic structures and mobility dynamics. So, we propose BRIDGE(Bridge GPS and Road Trajectories via Dual Cross-Modal Masked Reconstruction), a trajectory representation learning framework based on dual cross-modal masked reconstruction. Unlike prior methods that focus only on unidirectional interaction, BRIDGE enables bidirectional information flow between modalities and enhances representational alignment through cross-modal contrastive learning. Hence, BRIDGE could integrate dynamic behavior modeling and spatial structure constraints to address the semantic bias issues in traditional unimodal methods. Experimental results demonstrate that BRIDGE significantly outperforms 9 baseline methods across trajectory classification, travel time estimation, and trajectory retrieval tasks, with an average improvement of 6.9%. Trajectory Representation Multimodal Dual Cross-Modal Reconstruction Self-Supervised Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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