Determination of Influence of Competitiveness Factors on Freight Transportation by Water Transport

Abstract

Topicality. The relevance of the study is due to the fact that in the current crisis, which causes significant changes even in traditional economic activities, there are new factors of influence, there are multicollinear groups of factors that increase each other's influence, and these trends are not yet sufficiently disclosed in scientific literature. Therefore, there is an extremely important task, not related to the traditional assessment of the weight of the influence of known factors, but the identification of new factors, finding out the tendency to correlate the influence of two or more factors. This can be critical in making relevant predictions.
 Aim and tasks. The main purpose of the study is to develop mathematical methods for detection both identified and unidentified at the entrance to the problem factors influencing the competitiveness of water transport. To achieve this goal, the following tasks arose: development of a mathematical formalization of the separation of aperiodic and background effects on the resulting function; creation of formal approaches to the processing of primary data and results obtained using the developed mathematical model to reduce relative error and obtain relevant results.
 Research results Theoretical and applied provisions of increase of efficiency of cargo transportations by water transport taking into account factors of its competitiveness are investigated. In contrast to traditional approaches, which are based on a previously identified set of factors influencing the resulting function, the task of the study was to develop mathematical methods for detection both identified and unidentified factors influencing the competitiveness of water transport by its types. For this purpose, the original modification of the time series method was used. Using the developed mathematical model, the analysis of volumes of cargo transportation by water transport by its types is carried out. Additional methodological tools were used to adjust the forecast values for the next period of time. The use of the developed approaches indicated their practical value for leveling the background effects of external factors and aperiodic harmonics, which allowed to linearize the study time series, identify groups of influencing factors and specify the most important factors that allow to use the competitive advantages of water transport in Ukraine.Conclusion. Analysis of the results of the study allows us to draw the following conclusions: the presented mathematical model and the proposed methods of formalization allow us to develop using time series and available in the primary data of background and aperiodic harmonics relevant forecast data; the introduction of the developed mathematical model will reveal the impact on the resulting function of the efficiency of freight transportation of factors not identified in the problem. (возможно лучше такой вариант перевода Analysis of the results of the study allows us to draw the following conclusions: the presented mathematical model and the proposed methods of formalization allow us to develop using time series and available in the primary data of background and aperiodic harmonics relevant forecast data; introduction of the developed mathematical model will allow to reveal influence on the resulting function of efficiency of cargo transportation of the factors which have not been identified at statement of a problem.This will provide relevant forecast data for future periods, effectively promote the implementation of measures to use competitive advantages and neutralize threats.

Risk Assessment of Water Transport Enterprises by Modeling Direct and Indirect Threats

“…Thus, Kotenko et al (2021) consider a mathematical method for detection of unidentified factors that affect the competitiveness of water transport. For this purpose, a method of dividing aperiodic and background effects into the resulting function is proposed.…”

“…where 𝑊 -connectivity tensor; 𝑄 -the tensor of the probabilities of the presence of cargo flows at the points of transportation in the specified time intervals; 𝑉risk impact tensor (Kotenko et al, 2021).…”