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

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.