ИНТЕЛЛЕКТУАЛЬНАЯ СИСТЕМА КОНТРОЛЯ ЗАТОРОВ НА ДОРОГАХ С ИСПОЛЬЗОВАНИЕМ КОНТРОЛИРУЕМОГО АЛГОРИТМА МАШИННОГО ОБУЧЕНИЯ НА БАЗЕ АДАПТИВНОГО IOTN

Abstract

The phenomenon of congestion on the roads occurs when the demand rate on the road or ona transport facility exceeds the available capacity, and there are two types: either routine, i.e.occurs at certain times that are peak, for example, on the road, walking or returning from work oreducational institutions of people; or another type – sudden traffic jams that have appeared as aresult of a traffic accident, that is, in the event of an accident on the road, or due to other forcemajeure reasons. In this regard, in order to reduce the increase in congestion in cities, it is possibleand necessary to use the concept of smart systems in modern conditions of life and technologydevelopment. It is distinguished by a variety of algorithms used in the world of machine learning(ML) and the Internet of Things (IoT) to more accurately predict the flow of traffic in the shortterm and identify opportunities to prevent congestion. In modern cities, many different sensors canbe used to collect information to predict short-term traffic in the city and accurately capture thespatial and temporal evolution (change) of traffic flow. Algorithms embedded in machine learningimprove the capabilities of the system being developed. The quality of the decisions made by thedeveloped artificial intelligence increases with a simultaneous increase in the volume of data collected.This article proposes a model of the TCC-SVM system for analyzing traffic jams in a smartcity environment. The proposed model includes an Internet of Things (IoT) traffic managementsystem that reports congestion at a certain point. Existing traffic management systems are becomingineffective due to the increase in the number of vehicles on the roads. In urban areas, trafficjams and accidents are a serious problem. An intelligent transport system is necessary to solve theproblems caused by congestion on the roads.