Uncovering the Community Structure and Evolutionary Dynamics of On-demand Instant Delivery Networks

Uncovering the Community Structure and Evolutionary Dynamics of On-demand Instant Delivery Networks | 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 Uncovering the Community Structure and Evolutionary Dynamics of On-demand Instant Delivery Networks Chengbo Zhang, Zuopeng Xiao, Yonglin Li, Chenglong Wang, Weicong Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6894638/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 On-demand instant delivery has increasingly become an integral component of urban logistic systems. Yet, the dynamic mobility pattern of the vast rider fleet introduces significant sustainability challenges. Mitigating these impacts hinges on adaptive transport management grounded in the delivery system's inherent daily regularities; however, this dynamic mobility landscape remains largely underexplored. Here, using a large-scale dataset from Beijing, we construct time-evolving instant delivery networks and employ a cross-time-layer community detection method to track the hourly evolution of their dynamic community structures. Our analysis identifies 160 distinct communities and profiles their life cycles, from emergence to dissolution. By examining node variability within these dynamic communities and linking them to spatial factors through machine learning model, we find that building density and residential population significantly enhance community stability, while the presence of online retail and service facilities increases variability, highlighting the mobility patterns shaped by the tension of both dynamic demand and stable supply. These findings reveal the underlying structure of delivery mobility, providing a data-driven framework for a crucial policy shift from static rules to adaptive management. We demonstrate this transition by strategies such as allocating delivery space based on identified temporal rhythms and optimizing fleet operations within the boundaries of these dynamic communities, ultimately fostering more efficient and sustainable urban logistics solutions. Humanities/Complex networks Physical sciences/Engineering Social science/Science technology and society On-demand logistics Instant delivery Dynamic community detection Transportation network Sustainable urban mobility Spatiotemporal analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Sep, 2025 Reviews received at journal 17 Sep, 2025 Reviews received at journal 24 Aug, 2025 Reviewers agreed at journal 02 Jul, 2025 Reviewers agreed at journal 02 Jul, 2025 Reviewers agreed at journal 02 Jul, 2025 Reviewers invited by journal 02 Jul, 2025 Editor assigned by journal 22 Jun, 2025 Submission checks completed at journal 17 Jun, 2025 First submitted to journal 14 Jun, 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-6894638","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":479942030,"identity":"bc4d4c6f-fb1e-4ce0-b53d-35db9f2c7f75","order_by":0,"name":"Chengbo Zhang","email":"","orcid":"","institution":"Harbin Institute of Technology Shenzhen","correspondingAuthor":false,"prefix":"","firstName":"Chengbo","middleName":"","lastName":"Zhang","suffix":""},{"id":479942031,"identity":"787dc291-09bf-422c-b8d6-917e73d80a42","order_by":1,"name":"Zuopeng 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