Adaptive Truncated Schatten Norm for Traffic Data Imputation with Complex Missing Patterns

Adaptive Truncated Schatten Norm for Traffic Data Imputation with Complex Missing Patterns | 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 Adaptive Truncated Schatten Norm for Traffic Data Imputation with Complex Missing Patterns Haopeng Deng, Fucheng Zheng, Kaixiang Ma, Xinhai Xia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6804022/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 Accurate traffic data imputation is essential for Intelligent Transportation Systems (ITS), particularly under complex missing patterns. This study presents the Adaptive and Truncated Schatten Norm Low-Rank Tensor Completion (LRTC-ATSN) model, which employs the flexible Schatten norm for low-rank approximation and introduces an adaptive truncation mechanism to suppress noise and retain core features. To address non-convex optimization, the model integrates the Adan algorithm with Nesterov momentum, achieving equilibrium between computational efficiency and recovery precision through dynamic parameter tuning. To evaluate performance, a framework was designed to simulate diverse real-world traffic scenarios with mixed missing patterns. Extensive experiments on datasets from Guangzhou and Seattle demonstrate that LRTC-ATSN outperforms existing methods, yielding 10.6% lower MAPE and 6.1% lower RMSE relative to the best baseline model. Even with 95.85% data loss, the model maintains high reliability. These results underscore LRTC-ATSN’s potential for enhancing ITS data robustness and applications across domains like finance and healthcare. Applied Mathematics Artificial Intelligence and Machine Learning Traffic Data Imputation Low-Rank Tensor Completion Missing Data Patterns Adaptive Update Non-Convex Optimization Truncated Schatten Norm Full Text Additional Declarations The authors declare no competing interests. Supplementary Files 0319.pdf Appendix 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6804022","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":465391074,"identity":"7123a7bf-ed92-4b14-93e8-d594dcbdb8ca","order_by":0,"name":"Haopeng Deng","email":"","orcid":"https://orcid.org/0009-0004-2164-4320","institution":"Guangzhou Maritime University","correspondingAuthor":false,"prefix":"","firstName":"Haopeng","middleName":"","lastName":"Deng","suffix":""},{"id":465391075,"identity":"6333c207-d136-427e-9e9c-b8daf172489a","order_by":1,"name":"Fucheng Zheng","email":"","orcid":"","institution":"Guangzhou Maritime 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