Efficient Planning for Safe Air Traffic Control With STL Constraints Using Deep Reinforcement Learning

Efficient Planning for Safe Air Traffic Control With STL Constraints Using Deep Reinforcement Learning | 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 Efficient Planning for Safe Air Traffic Control With STL Constraints Using Deep Reinforcement Learning Zengjie Zhang, Yunbo Huang, Jie Gao, Sofie Haesaert This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6328006/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 Air traffic control (ATC) is an important problem in aerial traffic management, which can be formulated as a planning problem with predefined signal temporal logic (STL) specifications. This formulation leads to a nonconvex mixed integer programming (MIP) problem which is computationally expensive due to the large amount of variables and constraints. However, the strict safety requirements and the time limitations of practical applications require solving feasible solutions efficiently. In this study, we enhance the efficiency of solving MIP by leveraging a large neighborhood search (LNS) algorithm, which aims to obtain near optimal solutions with reduced computational complexity by iteratively selecting subsets of variables and constraints to form smaller subproblems.. This subset is automatically chosen by a graph convolutional neural network (GCN) trained using a deep reinforcement learning (RL) algorithm with non-labeled data extracted from an off-the-shelf solver. We evaluate the proposed RL-based LNS approach on a dataset derived from a basic taxiing ATC scenario. Experimental results demonstrate that our method achieves a 68.6% reduction in computation time compared to the open-source heuristic solver SCIP, while maintaining high solution quality. Air traffic control deep reinforcement learning signal temporal logic graph convolutional network Full Text Additional Declarations No competing interests reported. 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-6328006","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":448718636,"identity":"331435d7-fbbf-4b5d-a0f9-22810e5792f3","order_by":0,"name":"Zengjie Zhang","email":"","orcid":"","institution":"Eindhoven University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zengjie","middleName":"","lastName":"Zhang","suffix":""},{"id":448718637,"identity":"b397a2d4-c572-4d6b-9472-8ea58c23fd14","order_by":1,"name":"Yunbo Huang","email":"","orcid":"","institution":"Eindhoven University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yunbo","middleName":"","lastName":"Huang","suffix":""},{"id":448718639,"identity":"9e2b22c8-6427-44b0-8fcd-87eb635ce7ce","order_by":2,"name":"Jie Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACxgYwJSEHJAwYHtgAKXbitFgY84C0JKQZMDAwE2dZRWIP0VqYG9gvfvjAIJG+XyJ544OEhD8M5oS0MDbwFEvOYJDI7ZFIKzZISDBgsGwmrCVBmvcfSEuOmUTiDwMGg8OEtST/5gE6jEcix/wHyBYitLAfkwZqSQBqMWMgTkszD5sl0C+GPWeeFUskJBjzENRi2N7++MYHhjp59vbkjR8+JMjJGRxvIKClmccARYCHgB0MDPIM7A8IKhoFo2AUjIIRDgDSEzhtO919YQAAAABJRU5ErkJggg==","orcid":"","institution":"Delft University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Jie","middleName":"","lastName":"Gao","suffix":""},{"id":448718642,"identity":"5bcb98cb-529f-421c-935a-ae8dbb61d22e","order_by":3,"name":"Sofie Haesaert","email":"","orcid":"","institution":"Eindhoven University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Sofie","middleName":"","lastName":"Haesaert","suffix":""}],"badges":[],"createdAt":"2025-03-28 12:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6328006/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6328006/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86808041,"identity":"9f894f22-b11a-48e6-88a1-7abc82ac731b","added_by":"auto","created_at":"2025-07-15 19:01:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":487885,"visible":true,"origin":"","legend":"","description":"","filename":"AMAIsubmission.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6328006/v1_covered_0857e211-a874-4ab8-97dc-108228251990.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Efficient Planning for Safe Air Traffic Control With STL Constraints Using Deep Reinforcement Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Air traffic control, deep reinforcement learning, signal temporal logic, graph convolutional network","lastPublishedDoi":"10.21203/rs.3.rs-6328006/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6328006/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Air traffic control (ATC) is an important problem in aerial traffic management, which can be formulated as a planning problem with predefined signal temporal logic (STL) specifications. 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