Correcting Loop Detector Positional Bias When Estimating Macroscopic Fundamental Diagrams

Correcting Loop Detector Positional Bias When Estimating Macroscopic Fundamental Diagrams | 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 Correcting Loop Detector Positional Bias When Estimating Macroscopic Fundamental Diagrams Nandan Maiti, Ludovic Leclercq This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5684680/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Mar, 2025 Read the published version in Data Science for Transportation → Version 1 posted 9 You are reading this latest preprint version Abstract This study explores the use of machine learning models (ML) to transform localized loop detector (LD) speeds into spatial link speeds for accurate estimation of network macroscopic fundamental diagrams ( NMFDs). We compare the performance of Ordinary Least Squares Regression (OLSR), Random Forest (RF), and XGBoost models, using a comprehensive dataset to evaluate their effectiveness in aligning LD speeds with floating car device (FCD) as ground truth average speed. The RF model showed the highest accuracy, with a 37.44% improvement over original LDD speeds and a Root Mean Squared Error (RMSE) of 9.54 km/hr. Additionally, we investigated the impact of LD positions and demand by separating loading and unloading periods. The RF model's adjustments were especially effective under these conditions, resulting in an improved accuracy of 44.9%. Our analysis of NMFDs further validated the RF model's robustness and accurate estimation of NMFDs using corrected LDD. Loop detectors Floating cars Spatial imputation Macroscopic fundamental diagrams Machine learning models Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Mar, 2025 Read the published version in Data Science for Transportation → Version 1 posted Editorial decision: Revision requested 06 Feb, 2025 Reviews received at journal 05 Feb, 2025 Reviews received at journal 14 Jan, 2025 Reviewers agreed at journal 24 Dec, 2024 Reviewers agreed at journal 24 Dec, 2024 Reviewers invited by journal 24 Dec, 2024 Editor assigned by journal 23 Dec, 2024 Submission checks completed at journal 23 Dec, 2024 First submitted to journal 20 Dec, 2024 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-5684680","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":394705174,"identity":"85650d1b-5901-44fc-9b0a-faecc63c17a8","order_by":0,"name":"Nandan Maiti","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIie3PMUsDMRTA8SeBdEnJesWqXyEinBye+kFcDgqdLo7i2Ckude9kv4Juji9krd5awUEoOJ1wbnUQfVG7pdfVIf8hhAc/XgIQi/3Tkp+TAWADIPwdL3PoA8NNhIGdrMhsSBdebCC0honVqI0cdh7s/OMezuSVtO7YPPcVMotY5ES6LyGSjc8H2fUM9MQxcNq8CoW8IDIk0lEhorBMk64BPfolTvSmtXLvSydgzwQfpqo67X0SmXqSeTKSDW35oi08TOZluu233HqyRUTSx4lgC6kPjnZMou8cU3b86AlXRAaCryNVuf/0ZnJ9U9lFs7xwpxzYosHiZFeuIX8lgVkriMVisVhr38tsXha2ME1sAAAAAElFTkSuQmCC","orcid":"","institution":"Université Gustave Eiffel","correspondingAuthor":true,"prefix":"","firstName":"Nandan","middleName":"","lastName":"Maiti","suffix":""},{"id":394705175,"identity":"902d338e-3aa2-45ff-89c8-7183248f0d28","order_by":1,"name":"Ludovic Leclercq","email":"","orcid":"","institution":"Université Gustave Eiffel","correspondingAuthor":false,"prefix":"","firstName":"Ludovic","middleName":"","lastName":"Leclercq","suffix":""}],"badges":[],"createdAt":"2024-12-20 14:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5684680/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5684680/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s42421-025-00120-w","type":"published","date":"2025-03-21T15:57:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79120696,"identity":"22b82aa0-e68c-4a77-978f-276c07c7090b","added_by":"auto","created_at":"2025-03-24 16:11:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8191152,"visible":true,"origin":"","legend":"","description":"","filename":"LDDspeedcorrectionsJournalSub.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5684680/v1_covered_550e21e4-ae4d-4fcd-9434-a1f96b1c89a6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correcting Loop Detector Positional Bias When Estimating Macroscopic Fundamental Diagrams","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"data-science-for-transportation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Data Science for Transportation](https://www.springer.com/journal/42421)","snPcode":"42421","submissionUrl":"https://submission.nature.com/new-submission/42421/3","title":"Data Science for Transportation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"snapp","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Loop detectors, Floating cars, Spatial imputation, Macroscopic fundamental diagrams, Machine learning models","lastPublishedDoi":"10.21203/rs.3.rs-5684680/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5684680/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study explores the use of machine learning models (ML) to transform localized loop detector (LD) speeds into spatial link speeds for accurate estimation of network macroscopic fundamental diagrams ( NMFDs). 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