Leveraging Machine Learning for Resilient Urban Transport Planning under Deep Uncertainty: A Case Study of Abuja, Nigeria

Leveraging Machine Learning for Resilient Urban Transport Planning under Deep Uncertainty: A Case Study of Abuja, Nigeria | 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 Leveraging Machine Learning for Resilient Urban Transport Planning under Deep Uncertainty: A Case Study of Abuja, Nigeria Ahmad Muhammad Salisu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7943537/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 systems in developing cities like Abuja, Nigeria, face growing uncertainties driven by rapid urbanization, fluctuating economic conditions, climate variability, and evolving technologies. Conventional planning models often rely on static assumptions that fail to capture these unpredictable dynamics. This study introduces a machine learning (ML)–based framework designed to enhance resilience in urban transport planning by identifying, modeling, and forecasting uncertain factors that affect mobility and accessibility in Abuja. The research integrates multiple data sources—traffic flow, satellite imagery, weather data, and socioeconomic indicators—to train predictive models using supervised and unsupervised ML techniques such as Random Forests and Gradient Boosting. These models are employed to detect complex, non-linear relationships between transport demand, infrastructure performance, and external stressors. Scenario-based simulations are then used to evaluate the robustness of policy interventions under diverse conditions, including population surges, fuel price volatility, and climate-related disruptions. Preliminary results indicate that ML algorithms significantly improve the accuracy of transport demand forecasting and enable adaptive, data-driven decision-making. The framework empowers planners to simulate policy alternatives and assess their long-term sustainability before implementation. In the context of Abuja, where transport challenges are compounded by rapid population growth and informal transit systems, this approach offers a pragmatic solution for developing responsive mobility strategies that can withstand deep uncertainty. The study’s contributions are twofold: it demonstrates the practical applicability of ML techniques in resource-constrained environments, and it provides actionable insights for policymakers seeking to design resilient and sustainable transport systems. Ultimately, the research advocates for the integration of intelligent decision-support tools into Nigeria’s urban planning processes to promote equitable, efficient, and environmentally sound mobility for all. Artificial Intelligence and Machine Learning Machine learning transport resilience Abuja sustainable mobility urban planning uncertainty analysis Full Text Additional Declarations The authors declare no competing interests. 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-7943537","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":534675369,"identity":"6543ab71-f89c-46d2-b792-b94a66f2c0df","order_by":0,"name":"Ahmad Muhammad Salisu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYDACCSDmYWCQYYPy5UDEgQdEaOGBaTEGa0kgRguMn9gAIvFp4Z/dnfjhzR87Hj7+MwYMP3Pq0ueHHX4ItMVOTrcBhyV3zm6WnNuWzMMmkWPA2LvtcO7G22kGQC3JxmYHcFhzI3eDNG8DM1ALjwED77YDuRtnJ4C0HEjchkOL/I3czb95/tTzsAEdxvh3W1264ez0D3i1GNzI3SbNw3YYGGI5Bsy825gT5KVz8NtiCNRiObftONBhaQWHZbcdNtwgnVNwIMEAt1/kgA678eZPtZx8/+GND99uq5OXn52++cOHCjs5nN5HBmA1BhCSCOVwIN9AiupRMApGwSgYCQAABrhfM76jfp8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0002-6811-2052","institution":"Bayero University Kano","correspondingAuthor":true,"prefix":"","firstName":"Ahmad","middleName":"Muhammad","lastName":"Salisu","suffix":""}],"badges":[],"createdAt":"2025-10-26 16:36:36","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7943537/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7943537/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94574327,"identity":"4c68fd86-e7a8-46e5-bc54-f2c7a7eabfbc","added_by":"auto","created_at":"2025-10-28 18:08:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":311881,"visible":true,"origin":"","legend":"","description":"","filename":"PaperSubmission.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7943537/v1_covered_3d16a44f-5415-4833-bdc2-0ac6819492dc.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eLeveraging Machine Learning for Resilient Urban Transport Planning under Deep Uncertainty: A Case Study of Abuja, Nigeria\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Bayero University Kano","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":"Machine learning, transport resilience, Abuja, sustainable mobility, urban planning, uncertainty analysis","lastPublishedDoi":"10.21203/rs.3.rs-7943537/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7943537/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eUrban transportation systems in developing cities like Abuja, Nigeria, face growing uncertainties driven by rapid urbanization, fluctuating economic conditions, climate variability, and evolving technologies. 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