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ERSÖZ, Cem

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Cem

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ERSÖZ
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    Centrality and connectivity analysis of the European airports: a weighted complex network approach
    (Taylor & Francis, 2023-02-23) Ersöz, Cem; Karaman, F.; Aviation Management; ERSÖZ, Cem
    This study aims to reveal the structure of the European Airport Network (EAN) using concepts from complex network theory by utilising 2019 passenger data collected from Eurostat. Initially, the EAN was explored by computing connectivity and centrality measures and their correlations. The community structure of the EAN was also examined by modularity maximisation using the relatively new Leiden algorithm. When the network was compared with simulated models, it was observed that the EAN had small-world and scale-free properties. To measure the hub performance of the airports, their binary betweenness centrality was compared with a weighted betweenness measure employing the ratio of geographical distance to passenger traffic between the nodes. A significant difference was observed between the two centrality measures.
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    Analysis of Turkey’s airport network structure and centrality in the opening-out period after the first wave of COVID-19: A complex network approach
    (Elsevier, 2022-12) Ersöz, Cem; Kılıç, Sena; Aldemir, Hüseyin Önder; Aviation Management; ERSÖZ, Cem; KILIÇ, Sena; ALDEMİR, Hüseyin Önder
    There are a few studies present analyzing air transport structures of the countries, regions, and the world by using complex network theory. Although Turkey has a complex air network with 56 airports, thorough research has not been carried out applying complex network analysis to reveal the structure of the airport network in Turkey yet. Furthermore, the fact that Turkey is a developing economy and the role of air transport in this development is undeniable, and its use as a transit area due to its geopolitical location, together with social and political factors, makes it important to understand this air transport network in detail. Consequently, in this paper, Turkey's Airport Network (TAN) topology was explored by implementing concepts of complex network theory using domestic and international passenger flight data gathered from FlightRadar during the opening period after the first wave of Covid-19. The average path length and clustering coefficient for connectivity performance and centrality metrics (degree, betweenness, and closeness) were computed and the network correlations were also measured and compared with simulated random, small-world (SW), and scale-free (SF) network models. Also, structural similarities and differences with the air networks of other countries are revealed.