Optimizing eVTOL Time Scheduling through Origin-Destination and Temporal Data Analysis

Optimizing eVTOL Time Scheduling through Origin-Destination and Temporal Data Analysis | 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 Optimizing eVTOL Time Scheduling through Origin-Destination and Temporal Data Analysis Carolina Rutili de Lima, Fernando J. O. Moreira, Marcos R. O. A. Maximo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6234188/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 This study introduces a novel data-driven methodology to optimize the scheduling and station placement of electric vertical take-off and landing (eVTOL) vehicles, addressing key challenges in urban air mobility (UAM). Using São Paulo's metropolitan area and New York City as case studies, the research integrates diverse datasets—including helicopter path data, heliports, Uber Movement data, taxi data, and Google API information—to analyze traffic patterns, origin-destination pairs, and peak usage times. The study identifies strategic locations for eVTOL stations and optimal flight schedules by employing clustering algorithms and proximity analysis. The primary contribution is a framework that generates a comprehensive dataset in .csv format, detailing routes and schedules tailored to urban mobility needs and supporting the planning and deployment of eVTOL operations. This scalable and adaptable approach enables integrating urban traffic data into eVTOL planning, advancing sustainable transportation systems, and enhancing the efficiency of UAM solutions worldwide. eVTOLS UAM Data Science Data-Driven Optimization 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. 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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-6234188","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":442221287,"identity":"331a0373-c6e4-43e1-8cf4-a14d9eacda25","order_by":0,"name":"Carolina Rutili de Lima","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYLCCBwYgkrnxQQKQMiBKSwJYGWOzQUIC0VrAJGObBAMxWvjbzz58kFBwR55/9sG2ioc/7BjMpQ/g1yJxJt3YIMHgmeGMc4ltNxISkhks+xLwazFgSGOTSDA4zLiBhxGkhZnB4AwBhxnwPwNrsQdpKUhIqCdCiwTElkSQFoaEhMOEtUjceMYM9Mvh5BlnGJslEtKO81j2ENDC35/G+ODDn8O2/T3MBz/+sKmWM+choAUDkKxhFIyCUTAKRgEWAAC5fT7OcdFuWwAAAABJRU5ErkJggg==","orcid":"","institution":"Aeronautics Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Carolina","middleName":"Rutili","lastName":"de Lima","suffix":""},{"id":442221290,"identity":"059deea0-90f3-404a-b6ec-63f03f2618bc","order_by":1,"name":"Fernando J. 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