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...

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Main Authors: Mincheul Bae, Soyeong Lee, Heesun Joo
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Land
Subjects:
Online Access:https://www.mdpi.com/2073-445X/13/12/2031
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author Mincheul Bae
Soyeong Lee
Heesun Joo
author_facet Mincheul Bae
Soyeong Lee
Heesun Joo
author_sort Mincheul Bae
collection DOAJ
description 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.
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spelling doaj-art-feaf45308f614e6b93c60ac28bb2b5052025-08-20T02:53:26ZengMDPI AGLand2073-445X2024-11-011312203110.3390/land13122031Analysis of Core Area Characteristics in Travel Networks Using Block ModelingMincheul Bae0Soyeong Lee1Heesun Joo2Department of Urban Engineering, Gyeongsang National University, Jinju-si 52828, Republic of KoreaDepartment of Urban Engineering, Gyeongsang National University, Jinju-si 52828, Republic of KoreaDepartment of Urban Engineering, Gyeongsang National University, Jinju-si 52828, Republic of KoreaThis 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.https://www.mdpi.com/2073-445X/13/12/2031block modelingcore areacharacteristicstravel networks
spellingShingle Mincheul Bae
Soyeong Lee
Heesun Joo
Analysis of Core Area Characteristics in Travel Networks Using Block Modeling
Land
block modeling
core area
characteristics
travel networks
title Analysis of Core Area Characteristics in Travel Networks Using Block Modeling
title_full Analysis of Core Area Characteristics in Travel Networks Using Block Modeling
title_fullStr Analysis of Core Area Characteristics in Travel Networks Using Block Modeling
title_full_unstemmed Analysis of Core Area Characteristics in Travel Networks Using Block Modeling
title_short Analysis of Core Area Characteristics in Travel Networks Using Block Modeling
title_sort analysis of core area characteristics in travel networks using block modeling
topic block modeling
core area
characteristics
travel networks
url https://www.mdpi.com/2073-445X/13/12/2031
work_keys_str_mv AT mincheulbae analysisofcoreareacharacteristicsintravelnetworksusingblockmodeling
AT soyeonglee analysisofcoreareacharacteristicsintravelnetworksusingblockmodeling
AT heesunjoo analysisofcoreareacharacteristicsintravelnetworksusingblockmodeling