Analysis of Core Area Characteristics in Travel Networks Using Block Modeling

This study analyzes inter-regional traffic patterns and network structures using origin–destination (OD) data. Block modeling, a method that clusters nodes performing similar roles within a network to identify functional regional structures, distinguishes passenger and freight patterns. Eigenvector centrality extracts central cities, while multiple regression analysis compares factors influencing flows in core areas. The findings reveal that (1) freight flows exhibit more active inter-regional movement than passenger flows, relying heavily on long-distance transport; (2) passenger hubs tend to be geographically central, whereas freight hubs are located in peripheral areas; and (3) passenger flows are shaped by regional characteristics, industrial structure, and infrastructure, while freight flows are influenced by regional characteristics, infrastructure, and land use patterns. Population density and industrial facilities significantly impact both flow types. This study provides a comprehensive understanding of the distinct characteristics of passenger and freight flows, bridging gaps in the existing research. Moreover, it offers practical insights for policymakers aiming to promote balanced development and sustainable regional growth, emphasizing the integration of underdeveloped areas into broader strategies to address disparities and foster connectivity. By combining advanced analytical methods, this study establishes a novel framework for enhancing regional planning and policy formulation.