Time is considered as production resource, which determines the need for its effective use in connection with other production resources. The problem consists in the inconsistency of the results of the existing forecasting system of production which are founded on modern concepts of space and time. The theoretical positions of Galilean, A. Lorentz and G. Minkowski were analyzed. A. Einstein’s explanations regarding the use of the velocity of light in vacuum as a constant in the well-known dependency for the generalized four-dimensional space were studied. An attempt is made to solve the applied problem in space and time, taking as a constant, for example, standard or maximum possible, (calculated) productivity.
The paper substantiates the need to develop an automated system for traffic safety assessment in urban agglomerations, taking into account road conditions. The authors suggest a methodology for assessment of road traffic accidents, which makes it possible to take into account a wide range of factors affecting them. The methodology is based on complementing the traditional approach of final accident rate calculation with algorithms for collecting and analyzing data using Big Data tools, in particular, convolutional neural networks, fuzzy neural networks such as ANFIS, and cluster analysis using the k-means method. All accident rates are grouped according to the principle of homogeneity of acquisition of information for their calculation. Further, one of the data processing tools is applied to each group. As a result, labor intensity is reduced and the effectiveness of the application of the method of final accident rates increases. For practical calculations, the authors have developed a client-server application that uses data on geometric characteristics, current traffic situation, weather and climatic effects at the time of the trip along a specific itinerary. By means of application use, the analysis of traffic safety on a number of routes in Volgograd was carried out and the results are presented in comparison with the calculations made via the traditional method. It is shown that the use of information about the current situation on a specific section of the road network in terms of the current time significantly increases the accuracy of calculations.
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