Innovations and clustering as factors for increasing the efficiency of the country transport and logistics system

In the conditions of unstable transport flows, increasing requirements for the quality and speed of transportation, and the complication of logistics chains, the country transport and logistics system is inevitably transformed through the introduction of innovative technologies and restructuring. This determines the relevance of the problem of forming new approaches to organizing the interaction of transport and logistics entities, which will ensure the necessary pace of development of the country transport and logistics system in accordance with its strategic priorities and global economic challenges. One of these approaches is clustering and stimulation of innovative activities in the field of transport and logistics. However, despite the growing scientific interest in these issues, a methodological toolkit for the effective management of clustering and innovative development of the country transport and logistics system has not yet been formed. Aim. To identify the main factors that are interconnected with the efficiency of the transport and logistics system, expressed through the logistics performance index for 2023. Methods. Methods of formal logic and point assessment, horizontal, structural‐share and multi‐factor correlation analysis. Results. Using these methods the author has identified and studied the main characteristics of the system of transport and logistics clusters of Europe formed, a system of indicators for assessing the factors that can influence the efficiency of the transport and logistics system of the country and established the relationship of high and medium strength between the logistics performance index indicators. In addition, the author specified the concept of a transport and logistics cluster taking into account the characteristics considered. It was revealed that the efficiency of the transport and logistics system is largely associated with such clustering characteristics as innovativeness, structure of participants and maturity of clusters. The further direction of the study involves conducting a multi‐factor regression analysis of the factors under consideration in order to build a clustering model in the field of transport and logistics.