Contrasts in Sustainability between Hub-Based and Point-to-Point Airline Networks

Airline hubs are often defined as nodes with a high degree of connectivity. Connectivity is measured by the “degree” of the node. The degree distribution of hub networks tends to have a convex shape (curved towards the origin), while point-to-point networks have a higher number of high-degree nodes...

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Bibliographic Details
Published in:Sustainability 2023-10, Vol.15 (20), p.15111
Main Authors: O’Kelly, Morton E, Park, Yongha
Format: Article
Language:English
Subjects:
Air transportation industry
Aircraft
Airlines
Airports
Analysis
Aviation
Classification
Connectivity
Costs
COVID-19
Laws, regulations and rules
Sustainability
Sustainable development
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Summary:Airline hubs are often defined as nodes with a high degree of connectivity. Connectivity is measured by the “degree” of the node. The degree distribution of hub networks tends to have a convex shape (curved towards the origin), while point-to-point networks have a higher number of high-degree nodes and a concave shape. This study aims to classify airline networks based on their hub orientation, expanding our understanding of network differences. The analysis in this paper involves fitting a power-law distribution, determining the range of degree distribution, and calculating the distribution of betweenness. These analyses provide insight into the classification of each airline. Each measurement helps to clarify the ambiguity in other scores. The goal is to establish a small set of rules that can clearly distinguish between the main types of networks. The classification includes four types of networks: One-hub, P2P (point-to-point), Multi-hub, and Complex networks. There is a well-recognized empirical distinction between hub networks, which have a few places with large betweenness, and point-to-point cases, which have a larger number of places with moderate betweenness. The significance of these results in terms of geographic importance is demonstrated by sorting 284 different airline networks based on these dimensions. These findings are expected to provide valuable information about the resilience and recovery of a network, as networks with many long-range connections are particularly vulnerable to a decrease in traffic. Additionally, these results have implications for the ability of networks to recover from a downturn.
ISSN:2071-1050
2071-1050
DOI:10.3390/su152015111