Chaotic Traffic Flow Prediction Based on Phase Space Reconstruction and Graph Convolutional Networks

Chaotic Traffic Flow Prediction Based on Phase Space Reconstruction and Graph Convolutional Networks | 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 Chaotic Traffic Flow Prediction Based on Phase Space Reconstruction and Graph Convolutional Networks jiwen li, dai jiang, yang zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8103925/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 For the field of the chaotic traffic flow prediction based on reconstructed phase space with C-C algorithm, Shortcomings are still existed. Including: (1) existing high time complexity based on the block averaging; (2) low search domain range with hyper-paradigm and limited neighbor radius; (3) low search accuracy with series oscillation. Therefore, in this paper, an improved C-C algorithm is proposed for phase space reconstruction of chaotic traffic flow time series, which reduces the time complexity by eliminating the block averaging strategy, and expands the search domain range and improve the search accuracy by the fusion paradigm, the coefficient of variation (cv) and the correlation function mechanism called Eliminating block averaging and Fusing paradigm combine with making Variation coefficient and Correlation Function for C-C (EFVCFC-C). Wavelet De-noising Algorithm (WDA) removes traffic flow data noise and reduces the volatility of statistics in the algorithm. The neural network adopts Temporal Graph Convolutional Network (T-GCN) and constructs the EFVCFC-C+T-GCN model for chaotic traffic flow prediction. Validation is performed using PeMS04 and PeMS08 traffic flow data. Compared to the prediction results of the models such as the LSTM, GRU, GCN, and the EFVCFC-C+T-GCN with noising. The experiments show that the root mean square error (RMSE) is optimized by 76.93%, 76.56%, 87.52%, 80.75% under PeMS04, and 70.28%, 76.36%, 89.75%, 71.82% under PeMS08. In summary, the EFVCFC-C+T-GCN prediction model improves the accuracy and sufficiency of extracting high-dimensional chaotic phase space features, and provides a reliable basis for analyzing chaotic characteristics in various fields such as traffic flow. Block averaging strategy Fusion paradigm Coefficient of variation Correlation function Temporal Graph Convolutional Network 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8103925","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":565972132,"identity":"4f70fcea-313e-4a91-99c1-d0e4a07ee36c","order_by":0,"name":"jiwen 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Including: (1) existing high time complexity based on the block averaging; (2) low search domain range with hyper-paradigm and limited neighbor radius; (3) low search accuracy with series oscillation. Therefore, in this paper, an improved C-C algorithm is proposed for phase space reconstruction of chaotic traffic flow time series, which reduces the time complexity by eliminating the block averaging strategy, and expands the search domain range and improve the search accuracy by the fusion paradigm, the coefficient of variation (cv) and the correlation function mechanism called Eliminating block averaging and Fusing paradigm combine with making Variation coefficient and Correlation Function for C-C (EFVCFC-C). Wavelet De-noising Algorithm (WDA) removes traffic flow data noise and reduces the volatility of statistics in the algorithm. The neural network adopts Temporal Graph Convolutional Network (T-GCN) and constructs the EFVCFC-C+T-GCN model for chaotic traffic flow prediction. Validation is performed using PeMS04 and PeMS08 traffic flow data. Compared to the prediction results of the models such as the LSTM, GRU, GCN, and the EFVCFC-C+T-GCN with noising. The experiments show that the root mean square error (RMSE) is optimized by 76.93%, 76.56%, 87.52%, 80.75% under PeMS04, and 70.28%, 76.36%, 89.75%, 71.82% under PeMS08. In summary, the EFVCFC-C+T-GCN prediction model improves the accuracy and sufficiency of extracting high-dimensional chaotic phase space features, and provides a reliable basis for analyzing chaotic characteristics in various fields such as traffic flow.","manuscriptTitle":"Chaotic Traffic Flow Prediction Based on Phase Space Reconstruction and Graph Convolutional Networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-29 04:00:10","doi":"10.21203/rs.3.rs-8103925/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aacf5d7f-ac5a-4678-858a-e8475daf8a53","owner":[],"postedDate":"December 29th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-03T13:42:41+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-03T13:55:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-29 04:00:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8103925","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8103925","identity":"rs-8103925","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}