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Community Structure Detection In The Evolution Of The United States Airport Network

Author

Listed:
  • EMIL GEGOV

    (School of Engineering and Design, Brunel University, Kingston Lane, Uxbridge UB8 3PH, United Kingdom)

  • M. NADIA POSTORINO

    (Department of Informatics, Mathematics, Electronics and Transport, University of Reggio Calabria, Reggio Calabria 89122, Italy)

  • MARK ATHERTON

    (School of Engineering and Design, Brunel University, Kingston Lane, Uxbridge UB8 3PH, United Kingdom)

  • FERNAND GOBET

    (School of Social Sciences, Brunel University, Kingston Lane, Uxbridge UB8 3PH, United Kingdom)

Abstract

This paper investigates community structure in the US Airport Network as it evolved from 1990 to 2010 by looking at six bi-monthly intervals in 1990, 2000 and 2010, using data obtained from the Bureau of Transportation Statistics of the US Department of Transport. The data contained monthly records of origin–destination pairs of domestic airports and the number of passengers carried. The topological properties and the volume of people traveling are both studied in detail, revealing high heterogeneity in space and time. A recently developed community structure detection method, accounting for the spatial nature of these networks, is applied and reveals a picture of the communities within. The patterns of communities plotted for each bi-monthly interval reveal some interesting seasonal variations of passenger flows and airport clusters that do not occupy a single US region. The long-term evolution of the network between those years is explored and found to have consistently improved its stability. The more recent structure of the network (2010) is compared with migration patterns among the four US macro-regions (West, Midwest, Northeast and South) in order to identify possible relationships and the results highlight a clear overlap between US domestic air travel and migration.

Suggested Citation

  • Emil Gegov & M. Nadia Postorino & Mark Atherton & Fernand Gobet, 2013. "Community Structure Detection In The Evolution Of The United States Airport Network," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 16(01), pages 1-21.
    • Handle: RePEc:wsi:acsxxx:v:16:y:2013:i:01:n:s0219525913500033
    DOI: 10.1142/S0219525913500033
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    Citations

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    Cited by:

    1. Bai, Bingfeng, 2022. "Strategic business management for airport alliance: A complex network approach to simulation robustness analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    2. Silvia Zaoli & Giovanni Scaini & Lorenzo Castelli, 2021. "Community Detection for Air Traffic Networks and Its Application in Strategic Flight Planning," Sustainability, MDPI, vol. 13(16), pages 1-16, August.
    3. Shengrun Zhang & Yue Hu & Xiaowei Tang & Kurt Fuellhart & Liang Dai & Frank Witlox, 2020. "Modeling the Evolutionary Mechanism of China’s Domestic Air Transport Network," Sustainability, MDPI, vol. 12(16), pages 1-18, August.
    4. Gao, Yi, 2021. "What is the busiest time at an airport? Clustering U.S. hub airports based on passenger movements," Journal of Transport Geography, Elsevier, vol. 90(C).

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