Smart City Traffic Optimization using IoD and IoT Integration

Smart City Traffic Optimization using IoD and IoT Integration | 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 Smart City Traffic Optimization using IoD and IoT Integration Aminu Yusuf, Tarek Sheltami, Ashraf Mahmoud, Muhammad Imam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7603039/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Urban traffic congestion, along with the resulting fuel waste (largely due to travel delays) and pollution, poses increasing challenges as city populations grow. There is a critical need for techniques to mitigate these effects while ensuring traffic efficiency. In this research, we propose a system that integrates Internet of Things (IoT) infrastructure and the Internet of Drones (IoD) to improve urban traffic management. IoT sensors monitor real-time traffic conditions, while Roadside Units (RSUs) collect and process data to deliver timely traffic updates to vehicles. Drones dynamically extend communication coverage by acting as mobile relay nodes, accelerating traffic information dissemination over larger areas, particularly those with sparse connectivity. To optimize drone placement for maximum coverage, the Particle Swarm Optimization (PSO) algorithm was employed. Using the SUMO simulator, we conducted experiments in two urban scenarios: Dammam (Saudi Arabia) and Doha (Qatar). Python and the Traffic Control Interface (TraCI) were used to implement functionalities such as the PSO, communication protocols, and dynamic rerouting. Results show that the system reduces vehicular emissions by up to 41% and travel times by up to 32% in Dammam, and up to 48% and 44% respectively in Doha, outperforming traditional Vehicle-to-Vehicle (V2V) systems used as a baseline. These outcomes demonstrate that integrating IoT and drones can minimize travel delays, reduce emissions, improve traffic flow, and ultimately enhance air quality while supporting sustainability in smart cities. Physical sciences/Engineering Physical sciences/Mathematics and computing IoT Drone-based Traffic Management Sustainable Urban Mobility Vehicle Rerouting Emission Reduction Intelligent Transportation Systems Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 28 Oct, 2025 Reviews received at journal 16 Oct, 2025 Reviews received at journal 12 Oct, 2025 Reviewers agreed at journal 02 Oct, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviewers agreed at journal 25 Sep, 2025 Reviewers agreed at journal 25 Sep, 2025 Reviewers invited by journal 25 Sep, 2025 Editor assigned by journal 24 Sep, 2025 Editor invited by journal 24 Sep, 2025 Submission checks completed at journal 23 Sep, 2025 First submitted to journal 23 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-7603039","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":523925287,"identity":"4d8b268f-bca7-4832-b6c2-65d348a2b639","order_by":0,"name":"Aminu 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There is a critical need for techniques to mitigate these effects while ensuring traffic efficiency. In this research, we propose a system that integrates Internet of Things (IoT) infrastructure and the Internet of Drones (IoD) to improve urban traffic management. IoT sensors monitor real-time traffic conditions, while Roadside Units (RSUs) collect and process data to deliver timely traffic updates to vehicles. Drones dynamically extend communication coverage by acting as mobile relay nodes, accelerating traffic information dissemination over larger areas, particularly those with sparse connectivity. To optimize drone placement for maximum coverage, the Particle Swarm Optimization (PSO) algorithm was employed. Using the SUMO simulator, we conducted experiments in two urban scenarios: Dammam (Saudi Arabia) and Doha (Qatar). Python and the Traffic Control Interface (TraCI) were used to implement functionalities such as the PSO, communication protocols, and dynamic rerouting. Results show that the system reduces vehicular emissions by up to 41\\% and travel times by up to 32\\% in Dammam, and up to 48\\% and 44\\% respectively in Doha, outperforming traditional Vehicle-to-Vehicle (V2V) systems used as a baseline. 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