基于WENA-MLST分析的航空运输网络空间组织映射

IF 3.6 2区 工程技术 Q2 TRANSPORTATION
Chengliang Liu , Bangjuan Wang , Hong Zhang
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引用次数: 0

摘要

由于航空枢纽的组织结构,如联系,层次结构,中心和辐条在全球范围内仍然是一个谜。因此,本研究旨在通过多尺度的加权自我网络分析(WENA)、ht指数和最大叶生成树(MLST)来揭示航空运输网络(ATN)中航空枢纽的空间组织。研究结果为了解ATN中不同区域城市的状况以及枢纽和相关辐条之间的关键关系提供了有价值的见解。结果表明,ATN是一个清晰的分层网络,并确定了三种类型的航空枢纽,即全球、国际和区域枢纽。此外,研究表明,ATN是由几个区域中心和辐条系统组成的,其中中心主导其子图,观察到范围和地理邻近效应。最后,还指出了航空枢纽作为国际门户和国内枢纽的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mapping the spatial organisation of air transport network by WENA-MLST analysis
Due to the airline hubs’ organisation such as linkage, hierarchical structure, and hub-and-spoke remains a mystery at a global scale. Thereby, the research aims to unravel the spatial organisation of airline hubs in the air transport network (ATN) by  using weighted ego network analysis (WENA), Ht-index, and maximum leaf spanning tree (MLST) at multi-scales. The findings provide valuable insights into the status of different regional cities in the ATN and the critical relationship between hubs and their associated spokes. The result shows that the ATN is a legibly hierarchical network, and three types of airline hubs, i.e. global, international, and regional hubs have been identified. Additionally, the study reveals that the ATN is made up of several regional hub-and-spoke systems, where hubs dominate their subgraphs, with range and geographical proximity effects are observed. Finally, the role of airline hubs as international gateways and domestic hubs is also noted.
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来源期刊
Transportmetrica A-Transport Science
Transportmetrica A-Transport Science TRANSPORTATION SCIENCE & TECHNOLOGY-
CiteScore
8.10
自引率
12.10%
发文量
55
期刊介绍: Transportmetrica A provides a forum for original discourse in transport science. The international journal''s focus is on the scientific approach to transport research methodology and empirical analysis of moving people and goods. Papers related to all aspects of transportation are welcome. A rigorous peer review that involves editor screening and anonymous refereeing for submitted articles facilitates quality output.
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