BUILDING A ROUTING MODEL USING COMPUTER VISION

Abstract

Over the years, the number of people and cars on public roads increases, the average time a person spends in motor vehicles, being in traffic jams, also increases due to the obsolescence of urban planning approaches in cities and the imperfection of systems for processing the flow of cars and traffic. There is an actual problem of urban traffic jams caused by a large number of cars. A solution needs to be proposed that involves the use of a computer vision system to collect and process data from vehicles, ultimately optimizing route recommendations to reduce congestion. The main components of the proposed system include data collection using various sensors and cameras, real-time computer vision analysis to monitor road conditions and detect traffic accidents, as well as the use of predictive algorithms to suggest alternative and more efficient routes to drivers.

The aim of the paper is to solve the problem of congestion in cities caused by excessive number of vehicles by proposing a system that uses computer vision technology to collect and process data from vehicles. The main goal of such a system is to provide more accurate and timely information to drivers, allowing them to make informed decisions about their routes and ultimately reduce road congestion, improve transport efficiency and minimize the environmental impact of urban travel.

The article discusses the problems associated with the growth of traffic in cities and current approaches to improving the situation on the routing of traffic flow. A description of computer vision technology is given as an opportunity to enhance routing algorithms by processing large data flows reflecting the real situation and the reasons affecting the optimization of the route.