Smart City Logistics: Leveraging AI for Last-Mile Delivery Efficiency

Smart City Logistics: Leveraging AI for Last-Mile Delivery Efficiency | 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Article Smart City Logistics: Leveraging AI for Last-Mile Delivery Efficiency Junbo Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6142887/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 Urban environments face significant challenges in last-mile delivery, which is a crucial component of logistics. To address these challenges, we introduce a framework called Smart City Logistics, utilizing artificial intelligence (AI) to enhance delivery efficiency. The framework optimizes routing and improves resource allocation by analyzing real-time data from multiple sources, including traffic patterns and user preferences. The system employs machine learning algorithms for predictive analytics, enabling it to adjust in response to evolving demand. Furthermore, we investigate the integration of AI with advanced infrastructure, such as smart traffic signals and autonomous delivery drones, fostering a more agile delivery network. Case studies across various cities reveal substantial improvements in both delivery speed and operational costs, showcasing AI's transformative impact on logistics and its potential for sustainable urban delivery solutions. Computer Architecture and Engineering Delivery Efficiency Resource Allocation Full Text Additional Declarations The authors declare no competing interests. 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-6142887","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":423121669,"identity":"9d43d1b4-aab6-45cc-b555-79c0653de967","order_by":0,"name":"Junbo Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBACxgYGNiBlgyoqQYSWNFRFeLUAAUjLYRK0MM/IPfbgY9v5On7p3oOPKxjsog0OMB+8zYPPYTPy0g1ntt2WkJxzLtnwDENy7oYDbMnW+LXkmEnzbrstYXAjx0yygeEAUAuPmTQRWs5J2N/IMf8J0cL/jRgtByQMJHLMGKG2sOHX0vPG3HDmv2TJGXfOJUs2GCTnzjzMZmw5B48Ww/Ycswcfztjx88/uPfixocIut+9488Mbb/BpaYCxJECOMQBiZjzKQUAezpLA5/5RMApGwSgY0QAAwrNKpAkSZvgAAAAASUVORK5CYII=","orcid":"","institution":"Carnegie Mellon University","correspondingAuthor":true,"prefix":"","firstName":"Junbo","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-03-03 05:42:30","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6142887/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6142887/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77667024,"identity":"6a1e6eb0-370b-4044-96c9-6de26035b09e","added_by":"auto","created_at":"2025-03-04 06:16:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":560976,"visible":true,"origin":"","legend":"","description":"","filename":"SmartCityLogisticsLeveragingAIforLastMileDeliveryEfficiency2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6142887/v1_covered_a6540dfa-88b7-4671-b9a0-b45c7ee747a1.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eSmart City Logistics: Leveraging AI for Last-Mile Delivery Efficiency\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Carnegie Mellon University","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":"Delivery Efficiency, Resource Allocation","lastPublishedDoi":"10.21203/rs.3.rs-6142887/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6142887/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban environments face significant challenges in last-mile delivery, which is a crucial component of logistics. 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