A data-driven approach to inferring travel trajectory during peak hours in urban rail transit systems

A data-driven approach to inferring travel trajectory during peak hours in urban rail transit systems | 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 A data-driven approach to inferring travel trajectory during peak hours in urban rail transit systems Jie He, Yong Qin, Jianyuan Guo, Xuan Sun, Xuanchuan Zheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6349757/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 Refined trajectory inference of urban rail transit is of great significance to the operation organization. In this paper, we develop a fully data-driven approach to inferring individual travel trajectories in urban rail transit systems. It utilizes data from the Automatic Fare Collection (AFC) and Automatic Vehicle Location (AVL) systems to infer key trajectory elements, such as selected train, access/egress time, and transfer time. The approach includes establishing train alternative sets based on spatio-temporal constraints, data-driven adaptive trajectory inference, and trave l trajectory construction. To realize data-driven adaptive trajectory inference, a data-driven parameter estimation method based on KL divergence combined with EM algorithm (KLEM) was proposed. This method eliminates the reliance on external or survey data for parameter fitting, enhancing the robustness and applicability of the model. Furthermore, to overcome the limitations of using synthetic data to validate the result, this paper employs real individual travel trajectory data for verification. The results show that the approach developed in this paper can achieve high-precision passenger trajectory inference, with an accuracy rate of over 90% in urban rail transit travel trajectory inference during peak hours. urban rail transit trajectory inference parameter estimation Expectation-Maximization algorithm Kullback-Leibler Divergence Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Oct, 2025 Reviews received at journal 06 Jul, 2025 Reviews received at journal 22 Jun, 2025 Reviewers agreed at journal 21 Jun, 2025 Reviewers agreed at journal 26 May, 2025 Reviewers agreed at journal 30 Apr, 2025 Reviewers invited by journal 29 Apr, 2025 Editor assigned by journal 27 Apr, 2025 Submission checks completed at journal 06 Apr, 2025 First submitted to journal 01 Apr, 2025 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-6349757","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449449111,"identity":"98a49d47-0ff8-4b67-8c45-92551fbdc949","order_by":0,"name":"Jie He","email":"","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"He","suffix":""},{"id":449449112,"identity":"f45f42a4-a5d1-47ea-9a8d-ca878a2bb34d","order_by":1,"name":"Yong Qin","email":"","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Qin","suffix":""},{"id":449449113,"identity":"509fe167-d853-4245-9fb4-c11f4e3b18bc","order_by":2,"name":"Jianyuan Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBACxgYQWXGAGczjIV7LmQPMPERrgehrO8BAvBbmGenXJD7Ou8NuL5HA+OBtG4O8OUELZuSUSc7c9oyZRyKB2XBuG4PhzgbCWtKkebcdBmlhk+ZtY0gwOECUljlgLey/idSSfkyatwFiCzNxWnreMFvOOAb0y5mHzZJzzkkYbiCkxbA9/eGNDzV3ktnbkw9+eFNmI0/QFsMJOQYgOhkaqxIE1AOBPP/xByDajrDSUTAKRsEoGLEAALgjPm8JusHvAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Jianyuan","middleName":"","lastName":"Guo","suffix":""},{"id":449449114,"identity":"076a6432-f91f-4a45-8b02-af0c1987971a","order_by":3,"name":"Xuan Sun","email":"","orcid":"","institution":"China Railway Signaling and Communication Research and Design Institute","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Sun","suffix":""},{"id":449449115,"identity":"0579ddeb-4af0-4dd9-afe7-befc5e93a596","order_by":4,"name":"Xuanchuan Zheng","email":"","orcid":"","institution":"Beijing Urban Construction Design \u0026 Development Group Co","correspondingAuthor":false,"prefix":"","firstName":"Xuanchuan","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2025-04-01 05:53:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6349757/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6349757/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81694179,"identity":"19395b56-a3a9-4334-a18e-be9bb53da09d","added_by":"auto","created_at":"2025-04-30 11:52:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":894078,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6349757/v1_covered_c1aa5c9e-3981-43b3-9afa-1d25e135b794.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A data-driven approach to inferring travel trajectory during peak hours in urban rail transit systems","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":"transportation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"port","sideBox":"Learn more about [Transportation](http://link.springer.com/journal/11116)","snPcode":"11116","submissionUrl":"https://submission.nature.com/new-submission/11116/3","title":"Transportation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"urban rail transit, trajectory inference, parameter estimation, Expectation-Maximization algorithm, Kullback-Leibler Divergence","lastPublishedDoi":"10.21203/rs.3.rs-6349757/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6349757/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRefined trajectory inference of urban rail transit is of great significance to the operation organization. In this paper, we develop a fully data-driven approach to inferring individual travel trajectories in urban rail transit systems. It utilizes data from the Automatic Fare Collection (AFC) and Automatic Vehicle Location (AVL) systems to infer key trajectory elements, such as selected train, access/egress time, and transfer time. The approach includes establishing train alternative sets based on spatio-temporal constraints, data-driven adaptive trajectory inference, and trave l trajectory construction. To realize data-driven adaptive trajectory inference, a data-driven parameter estimation method based on KL divergence combined with EM algorithm (KLEM) was proposed. This method eliminates the reliance on external or survey data for parameter fitting, enhancing the robustness and applicability of the model. Furthermore, to overcome the limitations of using synthetic data to validate the result, this paper employs real individual travel trajectory data for verification. 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