Origin-Destination Demand Prediction for Shared Mobility Service Using Fully Convolutional Neural Network

Origin-Destination Demand Prediction for Shared Mobility Service Using Fully Convolutional Neural Network | 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 Origin-Destination Demand Prediction for Shared Mobility Service Using Fully Convolutional Neural Network Santi Phithakkitnukoon, Karn Patanukhom, Merkebe Demissie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4649879/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 Emerging on-demand shared mobility services face the difficulty of effectively balancing demand. Influx of these mobility services urges for more precise prediction of origin-destination demand becomes essential and urgent. Our previous work addressed this issue with a Masked Fully Convolutional Network (MFCN) model for short-term pick-up/drop-off demand prediction. In this study, we present a predictive modeling framework designed for short-term origin-destination demand prediction. This framework harnesses the capabilities of Convolutional Neural Networks (CNNs), integrates our previously developed MFCN model, and introduces novel prediction fusion and scaling methodologies. Furthermore, a new loss function is developed and designed to effectively train the model with demand and location information. We evaluated the proposed framework using shared e-scooter trip data from Calgary, Canada. Our evaluation encompasses two prediction scenarios: next-hour and next-24-hour predictions. The performance of our framework is benchmarked against baseline models including the naïve predictor, linear regression, GCN, and variant models. Our model shows the best performance regarding the true positive and F1-score values. The results suggest a high degree of regularity in the daily demand as the next-24-hour predictor performs better than the other scheme. Nonetheless, when a spatial error is considered, the performances of the two prediction schemes are comparable. Micromobility e-scooters on-demand mobility origin-destination trip estimation convolutional neural 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. 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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-4649879","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":324876673,"identity":"ae174cab-adeb-443e-937c-7df75ba218b8","order_by":0,"name":"Santi Phithakkitnukoon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYLCChAoULg9BDYwNCWeQ+WzEaGFsI0WLfPvZ4w8ezrPJk3dgfrqZh8FOnkG+9wBeLQZn8hIbErelFRseYDO7zcOQbNjAxpeAXwtDjiFQy+HEjQ08bEAtzAlAhxngd1j/G6CWOf9hWuoJa2G4AbKl4UDifAawlsOEtRjceGM4I+FYcuIGZjazm3MMjhu2seUQcliOwccfNXaJ89ubn914U1Etz898hoDD4NYdBpPAaCFOPci6BqKVjoJRMApGwUgDAL2mP8mJyXRhAAAAAElFTkSuQmCC","orcid":"","institution":"Chiang Mai University","correspondingAuthor":true,"prefix":"","firstName":"Santi","middleName":"","lastName":"Phithakkitnukoon","suffix":""},{"id":324876674,"identity":"eea4e86b-11fb-46c2-9c77-ef3cdeee6af0","order_by":1,"name":"Karn Patanukhom","email":"","orcid":"","institution":"Chiang Mai University","correspondingAuthor":false,"prefix":"","firstName":"Karn","middleName":"","lastName":"Patanukhom","suffix":""},{"id":324876676,"identity":"7cdd28f8-360b-473a-9b18-d915189fb53e","order_by":2,"name":"Merkebe Demissie","email":"","orcid":"","institution":"University of Calgary","correspondingAuthor":false,"prefix":"","firstName":"Merkebe","middleName":"","lastName":"Demissie","suffix":""}],"badges":[],"createdAt":"2024-06-27 16:15:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4649879/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4649879/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74308370,"identity":"701fd3bd-9ce8-4628-aa61-bbaf27d109f7","added_by":"auto","created_at":"2025-01-21 01:16:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2304170,"visible":true,"origin":"","legend":"","description":"","filename":"odpredictiontransportation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4649879/v1_covered_65197e4d-097e-49c9-b253-b07f71ea3c5f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Origin-Destination Demand Prediction for Shared Mobility Service Using Fully Convolutional Neural Network","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":"Micromobility, e-scooters, on-demand mobility, origin-destination trip estimation, convolutional neural network","lastPublishedDoi":"10.21203/rs.3.rs-4649879/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4649879/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEmerging on-demand shared mobility services face the difficulty of effectively balancing demand. Influx of these mobility services urges for more precise prediction of origin-destination demand becomes essential and urgent. Our previous work addressed this issue with a Masked Fully Convolutional Network (MFCN) model for short-term pick-up/drop-off demand prediction. In this study, we present a predictive modeling framework designed for short-term origin-destination demand prediction. This framework harnesses the capabilities of Convolutional Neural Networks (CNNs), integrates our previously developed MFCN model, and introduces novel prediction fusion and scaling methodologies. Furthermore, a new loss function is developed and designed to effectively train the model with demand and location information. We evaluated the proposed framework using shared e-scooter trip data from Calgary, Canada. Our evaluation encompasses two prediction scenarios: next-hour and next-24-hour predictions. 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