Scenarios of the Degree of Centrality and Density of the Networks of the Main Brazilian Airports Between 2003 to 2020 / Cenários do Grau de Centralidade e Densidade das Redes dos Principais Aeroportos Brasileiros Entre 2003 a 2020
Resumen
The identification of air transport networks is relevant to the economic development of a city or region, through its passenger demands, and, in this case, there may be a difference between the formation of these networks in different periods and which are the main actors compared in a sample of seven airports. The aim of this study is to analyze the degree of centrality of the network of the seven largest Brazilian passenger airports. This sample is based on the analysis of network indicators for the collection and extraction of passenger data between destinations and airport origins by the National Civil Aviation Agency (ANAC). The Social Network Analysis (SNA) method was applied for the construction of networks through the application of @ Ucinet/Netdraw Version 6.716 software to understand the degree of centrality and the density of the network in the scenarios of 2003, 2007, 2015, 2018, and 2020. It was concluded that the conjuncture of the links and nodes within the scenarios of the hub-and-spoke airport networks of the flows is sustained, to a greater and lesser extent in the network, by an airport in the Brazilian midwestern region (Brasília) and two other airports in the southeastern region. For future contributions, the nine-month 2020 Covid-19 pandemic period was analyzed, bringing results such as reductions in the degrees of centrality and density of the network of these seven airports.
Keywords. Airports. Passengers. Hub-And-Spoke. SNA.
RESUMO
A identificação das redes de transporte aéreo é relevante para o desenvolvimento econômico de uma cidade ou região, por meio de suas demandas de passageiros, e, neste caso, pode haver uma diferença entre a formação dessas redes em diferentes períodos e quais são os principais atores comparados em uma amostra de sete aeroportos. O objetivo deste estudo foi analisar o grau de centralidade da malha dos sete maiores aeroportos brasileiros de passageiros. Esta amostra é baseada na análise de indicadores de rede para coleta e extração de dados de passageiros entre destinos e origens aeroportuárias pela Agência Nacional de Aviação Civil (ANAC). O método de Análise de Redes Sociais (SNA) foi aplicado para a construção de redes através da aplicação do software @ Ucinet / Netdraw Versão 6.716 para entender o grau de centralidade e a densidade da rede nos cenários de 2003, 2007, 2015, 2018, e 2020. Concluiu-se que a conjuntura dos elos e nós dentro dos cenários das redes hub-and-spoke airport nos fluxos é sustentada, em maior ou menor grau na rede, por um aeroporto da região centro-oeste brasileiro (Brasília) e outros dois aeroportos da região sudeste do Brasil. Para contribuições futuras o período pandêmico da Covid-19 de nove meses de 2020 foi analisado, trazendo resultados como as reduções nos graus de centralidade e densidade da rede destes sete aeroportos.
Keywords. Aeroportos. Passageiros. Hub-And-Spoke. SNA.
Referencias
Alkaabneh, F., Diabat, A., & Elhedhli. (2019). A Lagrangian heuristic and GRASP for the hub-and-spoke network system with economies-of-scale and congestion. Transportation Research Part C, 102, 249–273.
Allroggen, F., Wittman, M. D., & Malina, R. (2015). How air transport connects the world – A new metric of air connectivity and its evolution between 1990 and2012. Transportation Research Part E, 80, 184-201. 2015.
Agência Nacional de Aviação Civil (ANAC). (2019). Base de dados completa. Dados Estatísticos*. Base de dados subdivida por ano 2000, 2009, 2015; 2018. Passageiros (Origem/Destino), Aeronaves (Pousos/Decolagens).
Ball, M., Barnhart, C., Nemhauser, G., & Odoni, A. (2006). Air transportation: irregular operations and control. In: Handbooks of Operations Research and Management, North-Holland.
Borgatti, S. P. (2002). NetDraw. Graph Visualization Software. Harvard: Analytic Technologies.
Borgatti, S. P., Everett. M.G., & Freeman, L. C. (2002). Ucinet for Windows Software for Social Network Analysis.
Brueckner, J. K., & Lin, M. H. (2016). Convenient flight connections vs. airport congestion: Modeling the ‘rolling hub’. International Journal of Industrial Organization, 48, 118–142.
Burghouwt, G., & Wit, J. (2005). Temporal Configurations of European Airline. Journal of Air Transport Management, 11(3), 185-198.
Button, K. (2002). "Debunking some common myths about airport hubs. "Journal of Air Transport Management, 8(3), 177- 202.
Campbell, J. F. (1994). Integer programming formulations of discrete hub location problems. European Journal of Operational Research, 72, 387–405.
Campbell, F. J., Stiehr, G., Ernst, A. T., & Krishnamoorthy, M. (2003). Solving hub arc location problems on a cluster of workstations. Parallel Computing, 29, 555-557.
Campbell, A. M., Lowe, T. J., & Zhang, L. (2007). The p-hub center allocation problem. European Journal of Operational Research, 176, 819-821, 834.
Fageda, X., & Flores-Fillol, R. (2017). A note on optimal airline networks under airport congestion. Economics Letters, 128, 90–94.
Feng, B., Li, Y., & Shen, Z. J. (2015). Air cargo operations: Literature review and comparison with pratices. Transportation Research Part C, 56, 263-280.
Hanneman, R. A. (2001). Introducción a los Métodos de Análisis de Redes Sociales. Departamento de Sociologia de la Universidade de Califórnia, Riverside, US, 150.
Hanneman, R. A., & Riddle, N. (2005). Introduction to SOCIAL Network Methods. Riverdise, CA: University of California, Riverside.
International Civil Aviation Organization - ICAO. (2020). Retrieved from https://www.icao.int/Pages/default.aspx
Janic, M. (2005). Modeling the large-scale disruption of an airline network. Journal Transport Engineer, 131, 249–260.
Lin, M. H., & Yimin, Z. (2017). Hub-airport congestion pricing and capacity investment. Transportation Research Part B: Methodological, 101, 89-106.
Mohri, S. S., Karimi, H., Kordani, A. A., & Nasrollahi, M. (2018). Airline hub-and spoke network design based on airport capacity envelope curve: A practical view. Computers & Industrial Engineering, 125, 375-393.
O'Kelly, M. E., & Bryan, D. L. B. (1998). "Hub location with flow economies of scale". Transportation Research B, 32(8), 608.
Ryerson, M. S., Kim, & H. (2013). Integrating airline operational practices into passenger airline hub definition. Journal of Transport Geography, 31, 84-63.
Rodríguez-Déniz, H., Suau-Sanchez, P., & Voltes-Dorta, A. (2013). Classifying airports according to their hub dimensions: an application to the US domestic network. Journal of Transport Geography, 33, 188-195.
Scott, J. (2000). Social Network Analysis. A handbook. New York. SAGE Publications Ltda, 2 edition.
Taherkhani, G., & Alumur, S. A. (2019). Profit maximizing hub location problems. Omega, 86, 1–15.
Veldhuis, J., & Kroes, E. K. (2002). Dynamics in relative network performance of the main European hub airports. European Transport Conference, Cambridge.
Yu, A., Yu, Z., & Bo, Z. (2015). The reliable hub-and-spoke design problem: Models and algorithms. Transportation Research Part B, 77, 103-122.
Wasserman, S., & Faust, K. (1994). Social Network Analysis: Methods and Applications. 1. ed. Cambridge: Cambridge University Press.
DOI: http://dx.doi.org/10.12819/2021.18.7.6
Enlaces refback
- No hay ningún enlace refback.
Este obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
Atribuição (BY): Os licenciados têm o direito de copiar, distribuir, exibir e executar a obra e fazer trabalhos derivados dela, conquanto que deem créditos devidos ao autor ou licenciador, na maneira especificada por estes.
Não Comercial (NC): Os licenciados podem copiar, distribuir, exibir e executar a obra e fazer trabalhos derivados dela, desde que sejam para fins não-comerciais
Sem Derivações (ND): Os licenciados podem copiar, distribuir, exibir e executar apenas cópias exatas da obra, não podendo criar derivações da mesma.
ISSN 1806-6356 (Impresso) e 2317-2983 (Eletrônico)