Analyzing the Factors Shaping Teleworking Decisions

Analyzing the Factors Shaping Teleworking Decisions | 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 Analyzing the Factors Shaping Teleworking Decisions Rakibul Hassan, Hamed Naseri, Sara Gharavi, Jean Dubé, Francesco Ciari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9061974/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Teleworking has become the new standard because of its sustained widespread adoption in recent times in compared to the period before covid pandemic. Thus, identifying the factors correlated with workers’ preferences for teleworking is important for transport policymakers. While previous studies mainly focused on socio-demographic factors, the study broadens the analysis by incorporating weather conditions and contextual variables. Specifically, it investigates the impact of 18 different factors on the propensity to telecommute in Quebec City, Canada. Due to the severe class imbalance, with only 7% of respondents reporting telecommuting in the pre-COVID Origin-Destination survey, the dataset was counterbalanced using under-sampling. Ten robust ensemble and machine learning algorithms were employed to predict whether the workers in Quebec City would choose teleworking or in-person work. Findings indicate that extreme gradient boosting outperforms all models, achieving approximately 94.23% accuracy on a test dataset and an F1 score close to 94.21%. The Shapley additive explanation (SHAP) is employed to capture the determinants of teleworking, and the results suggest that distance to work, age, temperature, household income, walkability of home location, and car per adult in the household have the most substantial relative influence on teleworking decisions. Further, Partial Dependency Plots are utilized to illustrate the direction influence of variables on teleworking decisions, and their outcomes show that the higher distances, age of 40–49, negative temperatures, higher household incomes, walk scores of over 90, and no access to cars are associated with a higher probability of teleworking preferences. Teleworking Weather Machine Learning Feature Importance analysis SHAP Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviews received at journal 11 Apr, 2026 Reviews received at journal 18 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers invited by journal 16 Mar, 2026 Editor assigned by journal 13 Mar, 2026 Submission checks completed at journal 13 Mar, 2026 First submitted to journal 07 Mar, 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-9061974","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":608052874,"identity":"eee281c4-f34e-4c02-8755-2b7b2d6b4126","order_by":0,"name":"Rakibul Hassan","email":"data:image/png;base64,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","orcid":"","institution":"Polytechnique Montréal","correspondingAuthor":true,"prefix":"","firstName":"Rakibul","middleName":"","lastName":"Hassan","suffix":""},{"id":608052875,"identity":"4685153a-a875-4410-8b70-4423832bd633","order_by":1,"name":"Hamed Naseri","email":"","orcid":"","institution":"Polytechnique Montréal","correspondingAuthor":false,"prefix":"","firstName":"Hamed","middleName":"","lastName":"Naseri","suffix":""},{"id":608052876,"identity":"40c20f09-d758-44fd-affd-ae36b54c060b","order_by":2,"name":"Sara Gharavi","email":"","orcid":"","institution":"Université Laval","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Gharavi","suffix":""},{"id":608052877,"identity":"6bf5af11-e99b-4886-b454-b5f5dccf45b7","order_by":3,"name":"Jean Dubé","email":"","orcid":"","institution":"Université Laval","correspondingAuthor":false,"prefix":"","firstName":"Jean","middleName":"","lastName":"Dubé","suffix":""},{"id":608052878,"identity":"6a6fcbd8-3291-409e-8562-d4221094dcc4","order_by":4,"name":"Francesco Ciari","email":"","orcid":"","institution":"Polytechnique Montréal","correspondingAuthor":false,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Ciari","suffix":""}],"badges":[],"createdAt":"2026-03-08 04:53:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9061974/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9061974/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104949634,"identity":"8c3be183-468c-4593-b3d5-38c1f4f405f2","added_by":"auto","created_at":"2026-03-19 06:27:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":710259,"visible":true,"origin":"","legend":"","description":"","filename":"AnalyzingtheFactorsShapingTeleworkingDecisionsISTDMSpecialIssue.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9061974/v1_covered_29601ac4-a51e-4b72-89ad-b3940a82b958.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analyzing the Factors Shaping Teleworking Decisions","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":"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":"Teleworking, Weather, Machine Learning, Feature Importance analysis, SHAP","lastPublishedDoi":"10.21203/rs.3.rs-9061974/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9061974/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTeleworking has become the new standard because of its sustained widespread adoption in recent times in compared to the period before covid pandemic. Thus, identifying the factors correlated with workers\u0026rsquo; preferences for teleworking is important for transport policymakers. While previous studies mainly focused on socio-demographic factors, the study broadens the analysis by incorporating weather conditions and contextual variables. Specifically, it investigates the impact of 18 different factors on the propensity to telecommute in Quebec City, Canada. Due to the severe class imbalance, with only 7% of respondents reporting telecommuting in the pre-COVID Origin-Destination survey, the dataset was counterbalanced using under-sampling. Ten robust ensemble and machine learning algorithms were employed to predict whether the workers in Quebec City would choose teleworking or in-person work. Findings indicate that extreme gradient boosting outperforms all models, achieving approximately 94.23% accuracy on a test dataset and an F1 score close to 94.21%. 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