ANALYSIS OF THE ROAD TRAFFIC MANAGEMENT SYSTEM IN THE NEURAL NETWORK DEVELOPMENT PERSPECTIVE

The research goal of the paper is to present the issues connected with road traffic management systems and to illustrate a management system that uses Intelligent Transportation Systems and neural networks. The use of Intelligent Transportation Systems (ITS) is a method of improving the conditions of communications, making it independent from the development of communications infrastructure. The attributes of neural networks are focused on solving the problems of optimisation, which involve the development of optimal strategies for traffic management. The proposed road traffic management system that uses ITS and neural networks can be applied in prediction of the conditions of communications in road traffic management. The paper presents the results of qualitative research carried out in the aspect of traffic volume forecasting on selected national roads, supported by a scientific search and discourse on logistic aspects of traffic management, with particular emphasis on Intelligent Transport Systems, in order to verify the effectiveness of the implementation of neural networks. The above mentioned issues are extremely important due to the necessity of knowing the expected load of routes. Traffic fluctuations related to factors such as time, traffic, road architecture and capacity utilization are important elements of traffic intensity. The study served to verify the effectiveness of four independent neural networks, forecasting the traffic volume, for seven days of the week, at particular time points. Empirical data utilised in presented qualitative research was derived from motion sensors, installed on selected national roads, at specific time intervals. It enabled prospects for the development of neural networks to be determined in a model perspective, constituting a set of artificial intelligence methods, in the context of vehicle traffic volume, which is characterised by certain repetitive regularities. The author's model of introducing an algorithm based on neural networks in application to measurements performed in transport in terms of quality, quantity and methods of data acquisition affects into the presented results. Different systems have been analyzed that are used to obtain data for modeling. As a result of various doubts, system maladjustments or excessive costs, alternative solutions have been proposed that can eliminate the presented problems. Solutions have been proposed that limit some of the problems reported by the authors in this regard. The presented research results justified the use of neural networks in measurements in transport. The results of the measurements were obtained in accordance with the actual observations and compared with the results of other systems. The authors analyze further required work and the possibilities of improving the solutions use