PREPARATION OF BIG DATA SETS FOR OPTIMIZATION OF INTERCITY FREIGHT DRIVER SCHEDULES

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.