Optimization of Route Planning and Clustering Using Road Network Distances: The Case of RouteIQ

Optimization of Route Planning and Clustering Using Road Network Distances: The Case of RouteIQ | 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 Optimization of Route Planning and Clustering Using Road Network Distances: The Case of RouteIQ Agus Rahmat Fadillah, Zico Pratama Putra, Dwiza Riana This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5899584/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 Due to the rapid expansion of new e-commerce trends, complex delivery networks and unparalleled consumer demands, logistics and supply chain operations are more sophisticated than ever before–making route optimization a require advancement. Such integrate real-world road network data and dynamic clustering method in route planning/clustering They introduce a system RouteIQ, designed to optimize planning. The RouteIQ engine does sophisticated real-time distance or road calculations using the Open Source Routing Machine (OSRM) to achieve dynamic route optimization. We implemented techniques such as the Traveling Salesman Problem (TSP) and AI-based clustering to improve delivery clusters, thus also addressing the variable traffic situations, closures of roads, or normal fluctuations in deliveries needed. Results show the model achieves operational efficiency improvements with 15 Percent less travel distance and optimal delivery times by 20 Percent. With the help of dynamic threshold-based clustering , cluster size and travel distance can be customized for balanced workload distribution and improved resource utilization. We used visualization tools (interactive maps and OSM-based heat maps) to generate actionable delivery patterns 1 including quick evaluations of clustering appropriate deliveries for effective assessment of clusters. The research notes the capability of RouteIQ to provide scalable and customization solutions for logistics optimization making delivery cost ineffective , organized with minimum manpower. Real-time traffic data integration to this algorithm, scalability to large crowds through cloud based solution and pre-dictive analytics for pro-active remedy measures are few of the future directions around which we could potentially build continued growth in adaptability with changing logistics needs. Route Optimization Open Source Routing Machine Traveling Salesman Problem Road Network Distances 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-5899584","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":428871935,"identity":"b34c747d-02c0-4594-a15d-2392729170e2","order_by":0,"name":"Agus Rahmat Fadillah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYBACNobDBwwSDCTk+OFChxkYDkCkErBq4WM8llDwocLGWLKBWC1yzGcMPs44k5a44QBMCM7AoYWN7YDhZt62w4ybb/cYf/iZY5fHd5z34QGGGjsGPnYcWngOJBsDtTCb3TljJtm7LblY8jC7wQGGY8lAqQfYtUgcOAbSwmZ2I8eMgXcbc+KGw2xAt7EBkQQOW+Qftv8GauExnpFj/PHvtnqoln94tAADyBDofQkDiRwDad5thyFaGNvwaTnGYAAMZAOJG2ll0rLbjifOBGlJ7EvmweUX+YbzH0BRWd8/I3nzx7fbqhP7zh9j/vDhm52cfDt2W3AAoGIeUtSPglEwCkbBKEAFAG4bZB8h07zeAAAAAElFTkSuQmCC","orcid":"","institution":"Nusa Mandiri University","correspondingAuthor":true,"prefix":"","firstName":"Agus","middleName":"Rahmat","lastName":"Fadillah","suffix":""},{"id":428871936,"identity":"ec591072-b39a-41b6-8f47-7bc2cf74278f","order_by":1,"name":"Zico Pratama Putra","email":"","orcid":"","institution":"Nusa Mandiri University","correspondingAuthor":false,"prefix":"","firstName":"Zico","middleName":"Pratama","lastName":"Putra","suffix":""},{"id":428871937,"identity":"6f033b1d-f7bb-47ce-ab91-4fda22e85f15","order_by":2,"name":"Dwiza Riana","email":"","orcid":"","institution":"Nusa Mandiri University","correspondingAuthor":false,"prefix":"","firstName":"Dwiza","middleName":"","lastName":"Riana","suffix":""}],"badges":[],"createdAt":"2025-01-25 05:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5899584/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5899584/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78711935,"identity":"9fa5fcdb-b37f-4b1c-9626-5bac14d4a470","added_by":"auto","created_at":"2025-03-18 01:32:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":393231,"visible":true,"origin":"","legend":"","description":"","filename":"RouteIQSpringerNature.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5899584/v1_covered_1dbe6911-69a7-4a1a-9ecd-aea5d18ec077.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimization of Route Planning and Clustering Using Road Network Distances: The Case of RouteIQ","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Route Optimization, Open Source Routing Machine, Traveling Salesman Problem, Road Network Distances","lastPublishedDoi":"10.21203/rs.3.rs-5899584/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5899584/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Due to the rapid expansion of new e-commerce trends, complex delivery networks and unparalleled consumer demands, logistics and supply chain operations are more sophisticated than ever before–making route optimization a require advancement. Such integrate real-world road network data and dynamic clustering method in route planning/clustering They introduce a system RouteIQ, designed to optimize planning. The RouteIQ engine does sophisticated real-time distance or road calculations using the Open Source Routing Machine (OSRM) to achieve dynamic route optimization. We implemented techniques such as the Traveling Salesman Problem (TSP) and AI-based clustering to improve delivery clusters, thus also addressing the variable traffic situations, closures of roads, or normal fluctuations in deliveries needed. Results show the model achieves operational efficiency improvements with 15 Percent less travel distance and optimal delivery times by 20 Percent. With the help of dynamic threshold-based clustering , cluster size and travel distance can be customized for balanced workload distribution and improved resource utilization. We used visualization tools (interactive maps and OSM-based heat maps) to generate actionable delivery patterns 1 including quick evaluations of clustering appropriate deliveries for effective assessment of clusters. The research notes the capability of RouteIQ to provide scalable and customization solutions for logistics optimization making delivery cost ineffective , organized with minimum manpower. 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