Estimating Annual Average Daily Traffic on Local Roads: Integrating Spatial Insights with Machine Learning

Estimating Annual Average Daily Traffic on Local Roads: Integrating Spatial Insights with Machine Learning | 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 Estimating Annual Average Daily Traffic on Local Roads: Integrating Spatial Insights with Machine Learning Liang Ma, Rami Al-Shukairi, Marc Stettler, Daniel Graham This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7189895/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 Obtaining street-level annual average daily traffic (AADT) is crucial for environmental assessments, infrastructure planning, and sustainable transport policy. However, comprehensive traffic data collection remains resource intensive and spatially sparse. We present a scalable machine learning (ML) framework that integrates over 900 contextual features (e.g. demographics) with engineered spatial statistical features (e.g. eigenvector spatial filtering) to predict AADT at over 19,000 locations across England and Wales. The framework is evaluated using two spatially informed cross-validation (CV) strategies: sampling-intensity-weighted and spatial block CV, achieving a test R² of 0.79 and 0.67, respectively. We systematically benchmark spatial feature contributions and show they enhance generalisation. Residual analysis confirms that the model effectively captures spatial dependencies. By integrating spatial statistical theory with scalable and interpretable ML pipelines, our framework addresses spatial autocorrelation and heterogeneity while remaining computationally efficient. The framework is broadly transferable to other spatial prediction tasks in environmental modelling, urban studies, and regional science. Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing Spatial Prediction Annual Average Daily Traffic Spatial Autocorrelation High-dimensional Data Explainable Machine Learning Full Text Additional Declarations No competing interests reported. Supplementary Files v302NatComptSciAADTpredSIsubmit.pdf 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-7189895","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":490785699,"identity":"726c940c-d197-4eb9-9f5f-301b96bc0bf5","order_by":0,"name":"Liang Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYBACPmYog42/+cABMIOBuQEsIoFDCxtMC5/EsQSoFkYCWmAMOYYcAyiTkBZ25mcPvzBsk2djOPPx0M0dNvl87AcbGH7UMCTObMDlMDZzYxmG24ZtzL0bDueeSbNs40lsYOw5xpA4G7dfzKQlGG4ztjGcBWppO2zAxpDYwMDbwJA4D6cW9m8gLfZtDDkPgFr+G7DxP2xg/ItXC4+Z5AeG24lALQxALQcM2CQSG5hBtuB2GE+ZNIPB7eQ2iWMGQL8kA7U8bDgsc0zCGJf3+fmPb5P8UXHbdn5/8+PPuTvsDOT7kw8+fFNjIzvjAA5rgICZBy1GGA7gjhWowh9wFg63jIJRMApGwcgGAPliVlEWGIboAAAAAElFTkSuQmCC","orcid":"","institution":"Imperial College London","correspondingAuthor":true,"prefix":"","firstName":"Liang","middleName":"","lastName":"Ma","suffix":""},{"id":490785700,"identity":"e8d82948-e42b-4807-b97a-a244b84ca347","order_by":1,"name":"Rami Al-Shukairi","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Rami","middleName":"","lastName":"Al-Shukairi","suffix":""},{"id":490785701,"identity":"ab53cfe4-4dbd-4518-8295-eacd5af968fa","order_by":2,"name":"Marc Stettler","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Marc","middleName":"","lastName":"Stettler","suffix":""},{"id":490785702,"identity":"5c170553-0ce8-470c-b171-780e3ca5434a","order_by":3,"name":"Daniel Graham","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Graham","suffix":""}],"badges":[],"createdAt":"2025-07-22 18:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7189895/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7189895/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88035671,"identity":"bf5f62cf-5c8f-41b2-9737-61fce833a0ac","added_by":"auto","created_at":"2025-07-31 16:17:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1382566,"visible":true,"origin":"","legend":"","description":"","filename":"v301NatComptSciAADTpredmanuscriptsubmit.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7189895/v1_covered_4e7637ab-0d29-4681-a5d2-74c2ad0ce7a2.pdf"},{"id":87969575,"identity":"252ae4d4-e5f3-4173-ac61-ab1edb404b41","added_by":"auto","created_at":"2025-07-31 02:36:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1972335,"visible":true,"origin":"","legend":"","description":"","filename":"v302NatComptSciAADTpredSIsubmit.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7189895/v1/5471e541cd46fee513a7b034.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimating Annual Average Daily Traffic on Local Roads: Integrating Spatial Insights with Machine Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Spatial Prediction, Annual Average Daily Traffic, Spatial Autocorrelation, High-dimensional Data, Explainable Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-7189895/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7189895/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Obtaining street-level annual average daily traffic (AADT) is crucial for environmental assessments, infrastructure planning, and sustainable transport policy. 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