МОДЕЛЬ ПРОГНОЗИРОВАНИЯ ТРАНСПОРТНОГО ПОТОКА НА ОСНОВЕ НЕЙРОННЫХ СЕТЕЙ ДЛЯ ПРЕДСКАЗАНИЯ ТРАФИКА НА ДОРОГАХ

Abstract

In connection with the industrialization of modern society, the growth of the transport systemsof our country, an increase in certain necessary for the development of the needs of the citizensof our country, the number of vehicles of various types continues to increase every year veryfast, causing huge traffic jams on transport roads, especially in large cities and megacities. Thus,forecasting traffic flows is an important and necessary component of optimal traffic control inmodern conditions of transport network development. As a solution to this problem, this articleaims to analyze and describe the application of artificial intelligence methods, in particular neuralnetworks, which represent a modern approach to modeling in complex and nonlinear situationsthat arise when predicting a traffic flow model. The shown accuracy method is based on the developmentof a neural network to predict the daily traffic flow. The expected traffic flow is then comparedwith the actual dataset recorded on the road section and provided by the infrastructuremanager. In fact, neural networks are able to learn from past situations and predict future situationson the transport network. In this study, various neural network structures were examined, and the simulation results showed that the best predictions were obtained using the multilayerperceptron architecture, which has a good generalization system with a root mean square error of0.00927 with the current set of vehicles. The first part of the article is devoted to defining variousconcepts related to the current research area, including a review of the literature on traffic predictionand neural networks. The second part is devoted to describing the problem of traffic congestionusing forecasting problems and presenting the proposed solution method with an emphasis onartificial neural networks as a means of forecasting demand and its various structures. Then, numericalexperiments are illustrated by analyzing the forecast results after the formation and testingof various neural network architectures.