MODELING INTERRELATIONS IN TRANSPORT SYSTEMS USING THE METHOD OF CORRESPONDENCE ANALYSIS

Abstract

The article explores the modeling of interrelations within transport systems through the application of Correspondence Analysis (CA), a statistical method particularly effective for categorical and mixed discrete data. The relevance of this research is shaped by the profound transformation of Ukraine’s transport network under the dual pressures of military aggression and global digitalization. These challenges necessitate innovative approaches to strategic and tactical decision-making in logistics, especially in the domain of intermodal transportation, which is increasingly recognized as a cornerstone of sustainable development. Intermodal transport enables the integration of rail, road, maritime, and inland waterways, thereby reducing costs, saving time, and minimizing environmental impact. The study systematizes the diverse factors influencing intermodal transport perfor- mance, including state policy support, infrastructure maturity, terminal network development, pricing mechanisms, and the role of ICT-driven innovations. A comprehensive review of contemporary literature highlights the shift from fragmented analyses toward systemic approaches that incorporate scenario planning, multi-criteria evaluation, and intelligent decision support sys- tems capable of processing large-scale datasets in real time. The methodological contribution of the paper lies in the proposed algorithm for applying CA to the segmentation of the intermodal transport market. This algorithm involves the construction of contingency matrices, normalization and profile building, χ²-distance calculations, singular value decomposition, and visualization of results in low-dimensional Euclidean space. Such an approach allows researchers and practitioners to uncover hidden associations, identify clusters of clients with similar modal preferences, and design differentiated tariff and service strategies tailored to specific market segments. Unlike traditional numerical methods, CA provides a more nuanced representation of categorical relationships, enabling the detection of structural patterns that are often overlooked in complex transport systems. The practical significance of the study is twofold. First, it offers a robust analytical tool for private stakeholders such as logistics providers, terminal operators, and intermodal carriers, who require accurate market segmentation to optimize service delivery. Second, it provides state institutions and policymakers with evidence-based insights for infrastructure planning, regu- latory frameworks, and sustainable transport strategies. By bridging methodological rigor with applied relevance, the research contributes to the modernization of transport systems and supports Ukraine’s integration into global logistics networks.