Optimization of train Real-time dynamic scheduling under unexpected events in High-speed railway based on MAPPO

Optimization of train Real-time dynamic scheduling under unexpected events in High-speed railway based on MAPPO | 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 Optimization of train Real-time dynamic scheduling under unexpected events in High-speed railway based on MAPPO Ziyue Tang, Piao Huang, jianping Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7542920/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract With the expansion of high-speed railway network scale and the increase of train operation density, the propagation effect of train delays triggered by emergencies such as equipment failures, natural disasters, and transportation safety incidents has become increasingly prominent. Traditional methods like the Mixed Integer Programming (MIP) model and fireworks algorithm optimization, while effective in stable environments, struggle to handle dynamic multi-train interaction scenarios. This paper innovatively proposes a dynamic scheduling framework based on multi-agent proximal policy optimization, aiming to minimize train delays. Through a distributed decision-making architecture, each train operates as an independent agent, leveraging 11-dimensional state features to achieve autonomous decision-making under local observation. A composite reward mechanism is designed, integrating punctuality incentives, safety distance constraints, overspeed rules, and rewards/penalties for minimum dwell time. A centralized training-decentralized execution collaborative paradigm is established to optimize group behavior coordination. Empirical studies demonstrate that this method significantly reduces delay propagation under unexpected events, achieving an average station punctuality rate of 56.3%, an average destination punctuality rate of 91.7%, and a destination punctuality rate of 100% for key trains, verifying its engineering applicability in complex transportation systems. Physical sciences/Engineering Physical sciences/Mathematics and computing Emergencies High-speed railroad Integrated reward mechanism MAPPO Real-time dynamic scheduling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 16 Oct, 2025 Reviews received at journal 15 Oct, 2025 Reviews received at journal 14 Oct, 2025 Reviews received at journal 14 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviews received at journal 23 Sep, 2025 Reviewers agreed at journal 23 Sep, 2025 Reviewers invited by journal 22 Sep, 2025 Editor assigned by journal 22 Sep, 2025 Editor invited by journal 16 Sep, 2025 Submission checks completed at journal 12 Sep, 2025 First submitted to journal 12 Sep, 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. 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-7542920","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":524013990,"identity":"591e8b1c-9bb2-468c-a5c8-f8ad09e909da","order_by":0,"name":"Ziyue 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