Application of Multi-Scale Traffic Data Integration with Life-Cycle Carbon Accounting and CTMC Deep Learning Model in Travel Behavior and Emission Reduction Policies

Application of Multi-Scale Traffic Data Integration with Life-Cycle Carbon Accounting and CTMC Deep Learning Model in Travel Behavior and Emission Reduction Policies | 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 Article Application of Multi-Scale Traffic Data Integration with Life-Cycle Carbon Accounting and CTMC Deep Learning Model in Travel Behavior and Emission Reduction Policies Fan Zihao, Shi Wenyan, Zhou Min, He Gang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7959783/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 Background California, USA, faces core challenges in transportation policy-making: insufficient data localization, poor policy effect adaptation to regional characteristics, and travel behavior prediction deviating from local habits. There is an urgent need to establish a transportation simulation and behavior prediction system integrating localized multi-source data to support precision governance( 1 ). Methods This study integrated multi-source localized data (e.g., Caltrans public transport fares, NOAA meteorological data) to construct a micro-meso-macro multi-scale traffic data integration framework. Based on 578,000 road network data entries and 50,000 real travel behavior data entries, 9 groups of traffic policy scenarios were designed for TraCI simulation, and an enhanced CNN-Transformer-Mamba travel behavior prediction model was proposed. Results Simulation results showed that the high congestion pricing policy achieved the optimal efficiency in California's core road network, with key simulation indicators optimized by over 1.73% compared to the baseline scenario. The carbon emission accounting results matched California's actual data with a consistency rate of 88.3% ( 5 ). In terms of behavior prediction, the NIO model exhibited the best comprehensive performance in static scenarios (accuracy = 0.9039), while the enhanced model maintained stability during dynamic peak hours (recall = 0.8984). Conclusions This study successfully built a data-driven decision-making framework, providing reliable support for the precise evaluation of California's transportation policies and localized travel behavior prediction. The research results can be directly applied to the optimization of intelligent transportation systems in cities such as San Francisco and Los Angeles. Physical sciences/Engineering Physical sciences/Mathematics and computing Multi-scale traffic data integration CNN-Transformer-Mamba (CTMC) deep learning model Life-cycle carbon accounting Travel behavior prediction Traffic emission reduction policies California transportation scenarios 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-7959783","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":549104040,"identity":"59a75986-59ef-403d-ad7f-973657c9567f","order_by":0,"name":"Fan Zihao","email":"","orcid":"","institution":"Nanning Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Zihao","suffix":""},{"id":549104041,"identity":"0626bb6a-310f-410e-9b54-da0d8be78f56","order_by":1,"name":"Shi 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Policies","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Multi-scale traffic data integration, CNN-Transformer-Mamba (CTMC) deep learning model, Life-cycle carbon accounting, Travel behavior prediction, Traffic emission reduction policies, California transportation scenarios","lastPublishedDoi":"10.21203/rs.3.rs-7959783/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7959783/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCalifornia, USA, faces core challenges in transportation policy-making: insufficient data localization, poor policy effect adaptation to regional characteristics, and travel behavior prediction deviating from local habits. There is an urgent need to establish a transportation simulation and behavior prediction system integrating localized multi-source data to support precision governance(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study integrated multi-source localized data (e.g., Caltrans public transport fares, NOAA meteorological data) to construct a micro-meso-macro multi-scale traffic data integration framework. Based on 578,000 road network data entries and 50,000 real travel behavior data entries, 9 groups of traffic policy scenarios were designed for TraCI simulation, and an enhanced CNN-Transformer-Mamba travel behavior prediction model was proposed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSimulation results showed that the high congestion pricing policy achieved the optimal efficiency in California's core road network, with key simulation indicators optimized by over 1.73% compared to the baseline scenario. The carbon emission accounting results matched California's actual data with a consistency rate of 88.3% (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In terms of behavior prediction, the NIO model exhibited the best comprehensive performance in static scenarios (accuracy\u0026thinsp;=\u0026thinsp;0.9039), while the enhanced model maintained stability during dynamic peak hours (recall\u0026thinsp;=\u0026thinsp;0.8984).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study successfully built a data-driven decision-making framework, providing reliable support for the precise evaluation of California's transportation policies and localized travel behavior prediction. The research results can be directly applied to the optimization of intelligent transportation systems in cities such as San Francisco and Los Angeles.\u003c/p\u003e","manuscriptTitle":"Application of Multi-Scale Traffic Data Integration with Life-Cycle Carbon Accounting and CTMC Deep Learning Model in Travel Behavior and Emission Reduction Policies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-24 12:51:07","doi":"10.21203/rs.3.rs-7959783/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":"fc035f72-bcd6-4170-9ce5-fa3c39129d0a","owner":[],"postedDate":"November 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58430127,"name":"Physical sciences/Engineering"},{"id":58430128,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-01-16T15:23:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-24 12:51:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7959783","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7959783","identity":"rs-7959783","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}