Communications - Scientific letters of the University of Zilina X:X | DOI: 10.26552/com.C.2026.022

Preparation of Big Data Sets for Optimization of Intercity Freight Driver Schedules

Olexandr Melnychenko ORCID...1, Myroslav Oliskevych ORCID...2,*, Nazar Khomyn ORCID...1
1 National Transport University, Faculty of Automotive Engineering, Department of Production, Repair and Materials Science, Kyiv, Ukraine
2 Stepan Gzhytskyi National University of Veterinary Medicine and Biotechnologies, Faculty of Mechanics, Energy and Information Technologies, Department of Automobiles and Tractors, Dubliany of Lviv Region, Ukraine

The problem of using big data for optimal planning of work and rest of truck drivers in intercity freight transportation is considered. The problem is solved for a team of drivers in compliance with the EU regulation 561/2006. The proposed data preparation can reduce the practical computational complexity and find the optimal driver work schedule. The clustering of the initial data was substantiated for optimizing schedules based on the criterion of compatibility of transport orders. The compatibility of orders follows from the analysis of similarity of two consecutive transportation processes performed by one driver during his/her work shift. Simulation modelling was performed. The influence of the scale of the territory on the quality of the prepared initial data was also investigated. A rational range of the compatibility coefficient for the high-quality preparation of a large data set was substantiated.

Keywords: road transportation, driver scheduling, big data, clustering, order compatibility
Grants and funding:

The authors received no financial support for the research, authorship and/or publication of this article.

Conflicts of interest:

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Received: January 12, 2026; Accepted: April 15, 2026; Prepublished online: April 15, 2026 

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