Exploring the Sources of Centrality in the Turkish Domestic Airport Network

Air passenger transportation in Turkey has been in rapid development since the mid-1980s due to the increasing investments in infrastructure and deregulating policies. This paper analyzed the network performances of Turkish airports and assessed the evolution from 2012 to 2020, from both topological and spatial perspectives. Unweighted and weighted graphs based on flight frequencies were used to determine airport centrality. Sabiha Gökçen (SAW) was defined as the most central airport in the domestic network and airport centrality showed significant improvement during the observation period. The weighted graph recorded significant losses in the centrality performances of airports, excluding eigenvector, during the pandemic process. Exogenous sources of airport centrality were also examined using Gradient Boosting Modeling. Results demonstrated the importance of the population and economic strength of the city in airport centrality, the decisive role of tourism demand and international flights on betweenness centrality, and the necessity of passenger terminal size on eigenvector centrality.

Keywords:

Complex network, Turkish airport system Airport centrality, Gradient Boosting Modeling,

PDF

___

Cai, W., Wei, R., Xu, L., & Ding, X. (2021). A method for modelling greenhouse temperature using gradient boost decision tree. Information Processing in Agriculture. https://doi.org/10.1016/j.inpa.2021.08.004
Cats, O. and Krishnakumari, P. (2020), Metropolitan rail network robustness, Physica A: Statistical Mechanics and its Applications, 549, 124317.
Chaouk, M., Pagliari, R. and Moxon, R. (2020), The impact of national macro-environment exogenous variables on airport efficiency, Journal of Air Transport Management, 82, 101740
Chen, Y., Wang, J. and Jin, F. (2020), Robustness of China’s air transport network from 1975 to 2017, Physica A, 539, 122876.
Cheng, B., Ma, Y., Feng, F., Zhang, Y., Shen, J., Wang, H., Guo, Y., Cheng, Y. (2021). Influence of weather and air pollution on concentration change of PM2.5 using a generalized additive model and gradient boosting machine. Atmospheric Environment, 255, 118437. https://doi.org/10.1016/j.atmosenv.2021.118437
Chi-Lok, A.Y. and Zhang, A. (2009), Effects of competition and policy changes on Chinese airport productivity: An empirical investigation, Journal of Air Transport Management, 15, 166–174.
Dahiya, N., Saini, B., & Chalak, H. D. (2021). Gradient boosting-based regression modelling for estimating the time period of the irregular precast concrete structural system with cross bracing. Journal of King Saud University - Engineering Sciences. https://doi.org/10.1016/j.jksues.2021.08.004
Dyer, A. S., Zaengle, D., Nelson, J. R., Duran, R., Wenzlick, M., Wingo, P. C., Bauer, J. R., Rose, K., Romeo, L. (2022). Applied machine learning model comparison: Predicting offshore platform integrity with gradient boosting algorithms and neural networks. Marine Structures, 83. https://doi.org/10.1016/j. marstruc.2021.103152
Erdem, U., Tsiotas, D, and Cubukcu, K.M. (2019), Population Dynamics in Network Topology: The Case of Air Transport Network in Turkey, Management Research and Practice, 11(2), 5-20.
Freeman, L. C. (1978). Centrality in Social Networks Conceptual Clarification. Social Networks, 1, 215–239.
Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. In Source: The Annals of Statistics (Vol. 29).
Friedman, J. H. (1999). Stochastic Gradient Boosting. Computational Statistics & Data Analysis, 38(4), 367–378. https://doi.org/10.1016/S0167-9473(01)00065-2
Kılıç Depren, S. and Gökalp Yavuz, F. (2018), The Network Analysis of the Domestic and International Air Transportation Structure of Turkey, Mugla Journal of Science and Technology, 4(2), 148-155.
Kosorukoff, A. (2011). Social Network Analysis: Theory and Applications. Retrieved from https://www. politaktiv.org/documents/10157/29141/SocNet_TheoryApp.pdf
Li, N., Huang, Q., Ge, X., He, M., Cui, S., Huang, P., Li, S, and Fung, S. F. (2021), A Review of the Research Progress of Social Network Structure. Complexity, Vol. 2021. https://doi.org/10.1155/2021/6692210.
Lin, J. (2012), Network analysis of China’s aviation system, statistical and spatial structure, Journal of Transport Geography, 22, 109-117.
Lordan, O., Sallan, J.M., Simo, P. and Gonzalez-Prieto, D. (2014), Robustness of the air transport network, Transportation Research Part E, 68, 155–163.
Newman, M. E. J. (2010), Networks: An Introduction. New York.
Newman, M. E. (2008), The mathematics of networks. The New Palgrave Encyclopedia Of Economics, 2(2008), 1-12.
Perez, C., and Germon, R. (2016), Graph Creation and Analysis for Linking Actors: Application to Social Data. In R. Layton & P. A. Watters (Eds.), Automating Open Source Intelligence. https://doi.org/10.1016/ B978-0-12-802916-9.00007-5
Sun, X. and Wandelt, S. (2021), Robustness of Air Transportation as Complex Networks: Systematic Review of 15 Years of Research and Outlook into the Future, Sustainability, 13, 6446.
Wandelt, S. and Sun, X. (2015), Evolution of the international air transportation country network from 2002 to 2013, Transportation Research Part E, 82, 55-78.
Wang, J., Mo, H., Wang, F. and Jin, F. (2011), Exploring the network structure and nodal centrality of China’s air transport network: A complex network approach, Journal of Transport Geography, 19, 712-721.
Wanke, P.F. (2013), Physical infrastructure and flight consolidation efficiency drivers in Brazilian airports: A two-stage network-DEA approach, Journal of Air Transport Management, 31, 1-5.
Wellman, B., and Leighton, B. (1979), Networks, Neighborhoods, and Communities: Approaches to the Study of the Community Question. Urban Affairs Quarterly, 14(3), 363–390.
Qiu, R., Liu, C., Cui, N., Gao, Y., Li, L., Wu, Z., Jiang, S., Hu, M. (2022). Generalized Extreme Gradient Boosting model for predicting daily global solar radiation for locations without historical data. Energy Conversion and Management, 258, 115488. https://doi.org/10.1016/j.enconman.2022.115488
Ridgeway, G. (2012). gbm: Generalized Boosted Regression Models, R package version 1.6-3.2.
Zhou, L., Fujita, H., Ding, H., & Ma, R. (2021). Credit risk modeling on data with two timestamps in peer- to-peer lending by gradient boosting. Applied Soft Computing, 110, 107672. https://doi.org/10.1016/j. asoc.2021.107672