A multiscale interpretability framework for identifying actionable road network features to mitigate congestion in highly congested cities

A multiscale interpretability framework for identifying actionable road network features to mitigate congestion in highly congested cities | 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 A multiscale interpretability framework for identifying actionable road network features to mitigate congestion in highly congested cities Nishant Kumar, Yatao Zhang, Nina Wiedemann, Jimi Oke, Martin Raubal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8846499/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Transferring congestion mitigation strategies across cities remains challenging due to two compounding issues: road network features affect congestion differently in different cities, and their influence may vary across spatial scales. We propose a multiscale framework to identify "actionable" road network features, defined as those maintaining a consistent directional relationship with traffic congestion across varying spatial resolutions. Through systematic investigation of seven highly congested cities worldwide, our findings reveal strong city-specific signatures in feature importance, indicating supply-side interventions must be tailored individually. However, the most effective features for demand-side interventions show notable similarities across cities. In the short term, our results directly impact ongoing measures like congestion pricing and personalized route choice applications, making them more robust and publicly acceptable. Long-term, our insights encourage re-evaluating skepticism toward infrastructure investments and highlight opportunities for designing congestion-resistant future cities. Given the diverse city selection, common observations identified can likely be extrapolated to new cities when detailed analysis is constrained by data limitations. We provide key recommendations for researchers. First, data-driven congestion studies must explicitly interrogate microscopic spatial scales, because coarse-scale models can conceal scale-dependent reversals in feature influence that directly undermine policy robustness. Second, simulation-based counterfactual approaches should test road network features beyond known value ranges. Scientific community and society/Geography Social science/Geography Physical sciences/Mathematics and computing Physical sciences/Physics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Apr, 2026 Reviews received at journal 02 Apr, 2026 Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 04 Mar, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviewers invited by journal 18 Feb, 2026 Editor invited by journal 13 Feb, 2026 Editor assigned by journal 11 Feb, 2026 Submission checks completed at journal 11 Feb, 2026 First submitted to journal 10 Feb, 2026 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-8846499","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":593241782,"identity":"4b0e6a03-ae71-4139-a398-623e95ada25c","order_by":0,"name":"Nishant Kumar","email":"","orcid":"","institution":"National University of Singapore","correspondingAuthor":false,"prefix":"","firstName":"Nishant","middleName":"","lastName":"Kumar","suffix":""},{"id":593241783,"identity":"ff4b2217-9b92-4593-a954-05ff6bcb5843","order_by":1,"name":"Yatao Zhang","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Yatao","middleName":"","lastName":"Zhang","suffix":""},{"id":593241784,"identity":"57febea3-4584-4c70-97d9-f5048be851fe","order_by":2,"name":"Nina Wiedemann","email":"","orcid":"","institution":"ETH Zurich","correspondingAuthor":false,"prefix":"","firstName":"Nina","middleName":"","lastName":"Wiedemann","suffix":""},{"id":593241785,"identity":"504abf8f-4fd4-4b03-918e-ffc1dcc77bd6","order_by":3,"name":"Jimi Oke","email":"","orcid":"","institution":"University of Massachusetts Amherst","correspondingAuthor":false,"prefix":"","firstName":"Jimi","middleName":"","lastName":"Oke","suffix":""},{"id":593241786,"identity":"54bdfe03-f062-4f5f-a610-b1cf34e154fc","order_by":4,"name":"Martin Raubal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYJACZgYGCzkGBh4Q+wDRWiSMGdhI1ZLYQLQW3dmHn0kX1Eik98v3Hv7AUHOHsBazc2lm0jOOSeTObONLMGA49owILWcYzKR52CRyNxzjMUhgbDhMjBb2b9I8/yTS7YFaDhCphcdMmrdNIsGAjcewgVgtxdYz+yQMZxzLMWZIOEacwzbeLvhmI8/ffMb4w4caIrSgggRSNYyCUTAKRsEowA4A8400ma5qWIwAAAAASUVORK5CYII=","orcid":"","institution":"ETH Zurich","correspondingAuthor":true,"prefix":"","firstName":"Martin","middleName":"","lastName":"Raubal","suffix":""}],"badges":[],"createdAt":"2026-02-11 03:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8846499/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8846499/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103504593,"identity":"6c6e8cba-f1b1-46b7-b0aa-428dd407ac6f","added_by":"auto","created_at":"2026-02-26 13:20:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":20021678,"visible":true,"origin":"","legend":"","description":"","filename":"UrbanscalesScientificReports2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8846499/v1_covered_ffd1250e-5fe1-45c3-8104-a70c55141f19.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A multiscale interpretability framework for identifying actionable road network features to mitigate congestion in highly congested cities","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8846499/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8846499/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Transferring congestion mitigation strategies across cities remains challenging due to two compounding issues: road network features affect congestion differently in different cities, and their influence may vary across spatial scales. 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