Analysing Train Delay Impacts on Subway Stations via a Three-stage Approach: An Empirical Study on Shanghai Metro

Analysing Train Delay Impacts on Subway Stations via a Three-stage Approach: An Empirical Study on Shanghai Metro | 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 Analysing Train Delay Impacts on Subway Stations via a Three-stage Approach: An Empirical Study on Shanghai Metro Yuxin He, Xiaoling Liu, Qi Zhang, Xinyun Liang, Jingjing Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4465702/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 Delays can adversely affect passengers, operational efficiency, and punctuality, necessitating effective methods to identify and analyse stations vulnerable to such delays. This study proposes a three-stage analytical framework. In the first stage, the 3σ rule is utilized to define normal passenger volume ranges and establish a specific time window, focusing on periods significantly affected by delays. Next, a multivariate time series clustering method is proposed to identify stations with stable demand and high volume, considering passenger volume differences both among and within stations. In the final stage, the effects of delays on these key stations are assessed by examining duration, starting, and ending times, and passenger volume variation, providing a comprehensive analysis of delay impact. The proposed method is validated on a real-world case: the 2021 delay incident at Shanghai Metro's Longyang Road Station. Our case study revealed that stations significantly impacted by delays are not limited to the specific line or direction of the delay, but also include opposite direction and network transfer stations. The starting time of effects at stations occurs in three stages—prior to, during, and after the delay—with full recovery at most key stations not occurring until the third time window. Additionally, both increases and decreases in passenger volumes due to the delay present considerable implications. Our results aid transit managers in better managing delays, thereby improving passenger satisfaction and operational efficiency. This paper also offers a comprehensive framework and an empirical study, providing valuable guidance for future research in this area. Urban rail transit Delay management Multivariate time series Passenger volume analysis Feature engineering Full Text Additional Declarations No competing interests reported. Supplementary Files SIforAnalysingTrainDelayImpactsonSubwayStationsviaathreestageapproachAnEmpiricalStudyonShanghaiMetro.pdf 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4465702","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":324874845,"identity":"295966fd-5e1a-4068-8ee4-26e4d63e78b8","order_by":0,"name":"Yuxin He","email":"","orcid":"","institution":"Shenzhen Technology University","correspondingAuthor":false,"prefix":"","firstName":"Yuxin","middleName":"","lastName":"He","suffix":""},{"id":324874847,"identity":"88702c86-c0f0-4f2f-b39d-92376622cb19","order_by":1,"name":"Xiaoling Liu","email":"","orcid":"","institution":"Shenzhen Technology 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