Uncertainty-Aware License Plate Recognition for Data-Driven Urban Traffic Analytics

Uncertainty-Aware License Plate Recognition for Data-Driven Urban Traffic Analytics | 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 Uncertainty-Aware License Plate Recognition for Data-Driven Urban Traffic Analytics Sujit Bhattarai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8900771/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 Urban transportation analytics increasingly rely on automated sensing infrastructures to generate actionable insights for planning and management. However, Automatic Vehicle Number Plate Recognition (ANPR) systems suffer performance degradation when applied to embossed license plates in developing-country environments, where illumination variability, bilingual scripts, and infrastructure idiosyncrasies introduce significant noise. This study proposes an uncertainty-aware, analytics-driven framework that integrates deep learning–based detection, bilingual optical character recognition, and structured urban intelligence modeling for end-to-end traffic analytics. We curate a bespoke dataset of 5,000 embossed Nepali license plate images captured across heterogeneous environmental conditions (daylight, dusk, low light, rain, and occlusion). Vehicle detection is performed using YOLOv8, and bilingual OCR is implemented via PaddleOCR with script-aware confidence calibration. Recognized events are aggregated to form structured event logs supporting statistical traffic density estimation and short-term forecasting using autoregressive integrated moving average (ARIMA) models with quantified uncertainty. Our empirical evaluation shows 98.2% detection accuracy under daytime conditions and 91.4% overall recognition accuracy, with statistically significant improvements over baseline configurations (t = 3.87, p = 0.0023). Forecasting results achieve MAE = 14.3 vehicles/hour and RMSE = 21.8 vehicles/hour, reducing forecasting error by 39.4% relative to a naïve persistence baseline, while prediction intervals capture 93% of observed values at 95% confidence. We further demonstrate that propagating recognition confidence into density estimation reduces aggregation bias under adverse conditions. The proposed framework extends conventional ANPR pipelines by embedding recognition uncertainty into downstream analytics, thereby providing a scalable data-driven foundation for intelligent transportation systems in resource-constrained urban environments. Automated Number Plate Recognition Traffic Forecasting Uncertainty Quantification ARIMA Modeling Intelligent Transportation Systems Structured Event Analytics Deep 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. 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-8900771","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611395775,"identity":"5239eb14-7ad6-44ca-843e-00c9875a6a18","order_by":0,"name":"Sujit Bhattarai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABNklEQVRIie2Rv0vDQBTHXwgky0HXczH/wpWCrdA/5gUhUzsJkqG2B0Jc6t4/oy6iky88SJeAawbBdHFyiLg4lOrFmsVEcRTMZ7mfH+773gG0tPxNkADeZreaiQqzVAAW5bsj9a1CYFtLK/Hjxec1wp8Vc6NU7LTH4jeKt8A1F+DYfYcUDzdDr++ahBie7oN7diWt6/uvisqwDCacwzkhj6OgezMPkDBd9UAkJ9JKH2uK/FCkgIyIx5pRZUKRHyW+lqMDaUXcEKxUlISHtebBplK2yUx7T40K7IKhUsTA4FSKnqB5ulFRaY6UKsI9nUB8YWpZpmUtCXUjERwP/KhWi3c+OirCkLADdy/Fq+mYWnGcF5Op13H5MnuOah0DENjYfxOyHNB8Wg23Yc8wrSbNxy0tLS3/iXemX4dUSpVRoQAAAABJRU5ErkJggg==","orcid":"","institution":"Tribhuvan University","correspondingAuthor":true,"prefix":"","firstName":"Sujit","middleName":"","lastName":"Bhattarai","suffix":""}],"badges":[],"createdAt":"2026-02-17 11:53:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8900771/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8900771/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105728117,"identity":"c2c06dfe-b32d-48ac-9105-c65d61926bd3","added_by":"auto","created_at":"2026-03-30 11:09:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":789762,"visible":true,"origin":"","legend":"","description":"","filename":"UncertaintyAwareLicensePlateRecognitionforDataDrivenUrbanTrafficAnalytics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8900771/v1_covered_6b33959a-5038-4cd9-ae39-438fd567018f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Uncertainty-Aware License Plate Recognition for Data-Driven Urban Traffic Analytics","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":"Automated Number Plate Recognition, Traffic Forecasting, Uncertainty Quantification, ARIMA Modeling, Intelligent Transportation Systems, Structured Event Analytics, Deep Learning","lastPublishedDoi":"10.21203/rs.3.rs-8900771/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8900771/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban transportation analytics increasingly rely on automated sensing infrastructures to generate actionable insights for planning and management. However, Automatic Vehicle Number Plate Recognition (ANPR) systems suffer performance degradation when applied to embossed license plates in developing-country environments, where illumination variability, bilingual scripts, and infrastructure idiosyncrasies introduce significant noise. This study proposes an uncertainty-aware, analytics-driven framework that integrates deep learning\u0026ndash;based detection, bilingual optical character recognition, and structured urban intelligence modeling for end-to-end traffic analytics.\u003c/p\u003e \u003cp\u003eWe curate a bespoke dataset of 5,000 embossed Nepali license plate images captured across heterogeneous environmental conditions (daylight, dusk, low light, rain, and occlusion). Vehicle detection is performed using YOLOv8, and bilingual OCR is implemented via PaddleOCR with script-aware confidence calibration. Recognized events are aggregated to form structured event logs supporting statistical traffic density estimation and short-term forecasting using autoregressive integrated moving average (ARIMA) models with quantified uncertainty.\u003c/p\u003e \u003cp\u003eOur empirical evaluation shows 98.2% detection accuracy under daytime conditions and 91.4% overall recognition accuracy, with statistically significant improvements over baseline configurations (t\u0026thinsp;=\u0026thinsp;3.87, p\u0026thinsp;=\u0026thinsp;0.0023). Forecasting results achieve MAE\u0026thinsp;=\u0026thinsp;14.3 vehicles/hour and RMSE\u0026thinsp;=\u0026thinsp;21.8 vehicles/hour, reducing forecasting error by 39.4% relative to a na\u0026iuml;ve persistence baseline, while prediction intervals capture 93% of observed values at 95% confidence. We further demonstrate that propagating recognition confidence into density estimation reduces aggregation bias under adverse conditions.\u003c/p\u003e \u003cp\u003eThe proposed framework extends conventional ANPR pipelines by embedding recognition uncertainty into downstream analytics, thereby providing a scalable data-driven foundation for intelligent transportation systems in resource-constrained urban environments.\u003c/p\u003e","manuscriptTitle":"Uncertainty-Aware License Plate Recognition for Data-Driven Urban Traffic Analytics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 09:02:24","doi":"10.21203/rs.3.rs-8900771/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":"cd00934e-5065-4a94-91ca-589987e33d1b","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-25T09:02:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 09:02:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8900771","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8900771","identity":"rs-8900771","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}