Search

The average speed and density of road transport are used as indicators of the congestion situation, but do not allow tracking the evolution (stages of formatting, progressing, and vanishing) of traffic congestion. The authors proposed and justified a quantitative indicator of traffic congestion, which allows in an automated mode to identify the congestion situation on the urban road network using hardware and software video recording systems. The purpose of this study is due to the need to study the quantitative characteristics of the proposed indicator at characteristic intersections of urban roads, which will allow us to develop scientifically based recommendations for predicting congestion situations, substantiating, and making optimal decisions on measures to promptly eliminate traffic congestion. The object of study is the traffic flow at three types of intersections of the Perm city road network, equipped with a photo and video recording software and hardware complex. The subject of the study is the regularities of the evolution of the listed deterministic indicators of traffic flows, which can be used for operational forecasting of the formation, development, and elimination of traffic congestion. The theoretical and methodological approach is based on the methods of mathematical statistics used to process the results of observations of traffic flows at different types of intersections using a «sliding window», calculating the average daily value and standard deviation. The initial data were obtained with the help of hardware and software complexes for fixing violations of traffic rules installed on the street and road network of the Perm city. As a result of the study, the rational parameters of the «sliding window» were determined, ensuring the structuring of the traffic congestion indicator; the facts of the congestion situations formation were revealed; the features of the congestion evolution and the presence of problematic traffic directions for which it is advisable to change the traffic light regulation mode were determined. The theoretical and practical significance of the work consists in checking the operability of the proposed indicator and criterion of traffic congestion, which is of practical interest from the point of view of predicting anomalies in the movement of vehicles on the road network, adjusting the operating modes of traffic lights, etc. It is also possible to use the proposed traffic congestion indicator to assess the effectiveness of traffic light regulation on the Perm city road network. The direction of further research is to study the patterns of traffic congestion at intersections of the urban road network, of various types that are not included in this study.

In the context of the formation of heterogeneous data that differ significantly in nature, even of a small volume, it becomes necessary to analyze them for decision-making. This is typical for many high-tech industrial fields of human activity. The problem can be solved by bringing heterogeneous data to a single view and then dividing it into clusters. Instead of searching for a solution for each data element, it is proposed to use the division of the entire set of normalized data into clusters, and thereby simplify the process of isolating the cluster and making a decision on it. The essence of the proposed solution is the automatic grouping of objects with similar data into clusters. This allows you to reduce the amount of analyzed information by combining a lot of data and perform mathematical operations already for the cluster. When splitting, it is proposed to use the theory of fuzzy logic. The possibility of such an approach is due to the fact that different objects always have several characteristics by which they can be combined. These signs are often not obvious and are poorly formalized. A hierarchical modification of the AFC fuzzy clustering method based on the operation (maxmin) of the fuzzy similarity ratio is proposed. The basic concepts and definitions of the proposed method of automatic partitioning of a set of input data, a step-by-step scheme of the corresponding cluster procedure are considered. The efficiency of the proposed method is demonstrated by the example of solving the problem of forming a traffic flow. A numerical experiment has shown that the developed algorithm allows you to automatically analyze heterogeneous data and stably divide them into classes. The application of the proposed modification allows for the preliminary partitioning of data into clusters and allows reducing the volume of analyzed data in the future. There is no need to consider the objects in each case separately.