Інтелектуалізація управління дорожнім рухом як засіб підвищення ефективності транспортної мережі міста в неординарних ситуаціях

Abstract

An assessment of modern trends in the development of intelligent traffic management systems and their role in ensuring the efficiency of the functioning of transport networks was carried out. The processes of introducing technologies for expanding the flow of processed data into existing intelligent transport systems (ITS) that ensure an increase in the speed of information transmission in them have been determined. The classification of information sources that become available when the ITS transitions to the 5G standard and provide a basis for the implementation of technologies for avoiding extraordinary situations in transport networks is given.Existing methods of improving the efficiency of the city's transport network are mainly aimed at ensuring the ability of ITS to predict traffic flows. These include statistical and nonlinear methods, simulation-based methods, artificial intelligence methods, and combined methods. The implementation of these methods is achieved by increasing the information flow coming from the system. A comparison of these methods revealed that they can generally make predictions with high accuracy, however, regardless of the chosen standard, some of them are already at the peak of their potential in terms of application in ITS, and the rest still have room for development.The suitability of the forecasting method for working in real-time conditions is a significant advantage in ensuring effective management of traffic flows, allows to increase the stability of the transport network and the efficiency of the ITS, and has a positive effect on the level of traffic jams, road safety and ecological impact on the environment. The most promising in terms of a quick and flexible solution to an extraordinary situation are models with the use of artificial intelligence or a combination thereof, based on deep learning algorithms, which have proven their importance in predicting the results, making decisions regarding traffic flow forecasts and ensuring the elimination and avoidance of traffic jams based on the passage of vehicles through the intersection depending on the length and duration of the traffic light signals.