Analysis of the Movement of Vehicles at the Intersections of the Urban Transport Network Based on Deep Learning

This article focuses on the analysis of vehicle movement at intersections in urban transportation networks using deep learning. The problem of optimizing traffic flow in cities, particularly related to intersection regulation, is a pressing issue that requires effective solutions. The second chapter of the article explores various methods for collecting raw data to build models for analyzing the congestion of urban road networks. Different approaches and technologies used in this process are analyzed. The third chapter provides an overview of deep learning models for object detection and recognition on images. Various models and architectures applicable for accurate and efficient analysis of traffic flows at intersections are discussed. The fourth chapter presents the methodology of the system, outlining the key steps and processes necessary for the successful implementation and operation of the vehicle movement analysis system at intersections. The methodology covers both technical and algorithmic aspects of the system. The conclusion summarizes the findings and describes the results of the developed system for analyzing vehicle movement at intersections. The presented results demonstrate the effectiveness of deep learning in optimizing traffic flow and improving intersection management in cities. The article makes a significant contribution to the field of traffic flow analysis and road movement by proposing a novel approach based on deep learning. The obtained results can be valuable for designing and optimizing urban transportation networks, reducing road congestion, and enhancing road safety.