Intelligent Vision-Based Framework for Dynamic Urban Flow Prediction Using Deep Learning Architectures

Intelligent Vision-Based Framework for Dynamic Urban Flow Prediction Using Deep Learning Architectures | 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 Intelligent Vision-Based Framework for Dynamic Urban Flow Prediction Using Deep Learning Architectures Yaswanth Sai Kamma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8662362/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 research introduces a vision-based computational framework that leverages deep neural networks to predict dynamic traffic flow patterns in complex urban environments. The proposed model integrates convolutional architectures and feature optimization strategies to enhance prediction accuracy under real-time constraints. By employing diverse image-based datasets and systematic evaluation metrics, the framework demonstrates robust performance across multiple experimental conditions. Comparative analysis of different deep learning models reveals key trade-offs in efficiency, precision, and scalability for large-scale deployment. The findings contribute to the advancement of intelligent transport analytics and real-time visual computing systems, reinforcing the role of deep learning in modern computer science applications for adaptive automation and smart city infrastructures. Artificial Intelligence and Machine Learning Computer Architecture and Engineering Deep Learning Computer Vision Traffic Prediction Convolutional Neural Networks Urban Analytics Smart Cities 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. 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