Abstract
Analyzing airports' role in global air transportation and monitoring their development over time provides an additional perspective on the dynamics of network evolution. In order to understand the different roles airports can play in the network an integrated and multi-dimensional approach is needed. Therefore, an approach to airport classification through hierarchical clustering considering several parameters from network theory is presented in this paper. By applying a 29year record of global flight data and calculating the conditional transition probabilities the results are displayed as an evolution graph similar to a discrete-time Markov chain. With this analytical concept the meaning of airports is analyzed from a network perspective and a new airport taxonomy is established. The presented methodology allows tracking the development of airports from certain categories into others over time. Results show that airports of equal classes run through similar stages of development with a limited number of alternatives, indicating clear evolutionary patterns. Apart from giving an overview of the results the paper illustrates the exact data-driven approach and suggests an evaluation scheme. The methodology can help the public and industry sector to make informed strategy decisions when it comes to air transportation infrastructure.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.