Research background: In determining the prices in road transport, carriers usually use the calculations based on a so-called routes utilisation coefficient, which allows the carrier to also take the possibility of the return rides without load into account. Currently, it is usually used as a constant from the interval from zero to one. Purpose of the article: Considering a different offer of return transport from individual European Union (EU) countries, it can be assumed that the routes utilisation coefficient should have different values because there is a varying level of non-zero probability that the vehicle will return without a load. This study therefore proposes a new approach to determining the value of this coefficient based on transport direction. The study also aims to identify clusters of EU countries, for which the common value of the coefficient should be set. Methods: The Analysis of Variance (ANOVA) test was used to verify the assumption of the differences among the means of transport offers. Cluster analysis was used to identify the aforementioned groups of countries. This analysis is based on real data on transport offers to Slovakia from 18 different EU countries. Findings & value added: The results of the analysis can also be used in other EU countries because if significant differences in transport offers to Slovakia exist in individual countries, there is a reasonable assumption that this conclusion will also be valid in other countries. The analysis demonstrated that it is more appropriate to use the routes utilisation coefficient with various values, dependent on the transport direction. For the transport companies, implementation of the obtained results into practice is beneficial to increase their competitiveness through the more precise setting of transport prices, but also to the optimisation of the transport price itself with regard to the expected costs.
The aim of this study is to develop a big data architecture that can provide the formation and management of competitive mixed passenger traffic in agglomerations in real time, taking into account the optimization of their cost, speed and new services. The work is based on studies of development trends of transport systems in agglomerations (SmartCitiesWorld); analysis of the methodology and use of systems for managing relational databases of structured data, as well as non-relational databases, information processing experience AIIM. For the analysis of passenger flows and decision-making on optimal routes, spatial databases OGS that implement standards have been used. The study substantiates the conclusion that the Big Data technology, implemented according to the cascade principle of information support for the transportation process, ensures the growth of monetization of all its components (transport infrastructure, vehicles and their management system). On this basis, a big data architecture was built, which implies the sharing of structured and unstructured data in the management of passenger traffic in agglomeration. This architecture made it possible to take into account the influence of the most important changes in the agglomeration on the mobility of its population and to optimize the financial performance of transport organizations due to the competitiveness of the allocated mixed routes.
The purpose of the paper is to justify the use of the intersectoral balance model for forecasting the demand for rail transportation in the face of significant changes in commodity markets. The theoretical basis of the study is the algebraic theory of the analysis of the input-output model. The methodology is based on the balance method. The modeling procedure comes down to solving a system of linear equations in which the coefficients are cost coefficients for production. The modeling information base includes scenario conditions for forecasting the socio-economic development of 40 industries and activities for which rail transportation is of decisive importance, and whose share in the structure of the GDP of Russia for the period until 2036 will change slightly. The main results of the study are: the implemented process of modeling demand for rail transportation, including a basic simulation of the intersectoral balance of the Russian economy for 2016, a predicted simulation model of demand for the period until 2030. An assessment was made of a long-term change in demand for rail transport services, taking into account structural changes affecting the activities of the main sectors of the national economy, which products are changing interspecific competition, the emergence of new product markets and digital technologies.
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