Dergiler / Journal of Transportation and Logistics / 2022 / Cilt: 7 - Sayı: 2

Exploring the Sources of Centrality in the Turkish Domestic Airport Network

Sayfa
199–212
Yayın Tarihi
2023-01-16
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Air passenger transportation in Turkey has been in rapid development since the mid-1980s due to the increasing investments in infrastructure and deregulating policies.
📊
Temel Bulgular & Veri:This paper analyzed the network performances of Turkish airports and assessed the evolution from 2012 to 2020, from both topological and spatial perspectives.
💡
Sonuç & Katkı: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.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2012 Verileri
⚙️Değişkenler:Complex network · Turkish airport system · Airport centrality

Abstract

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.

🔖

Bu Makaleye Atıf Yap

CODAL, K. S., GÜNER, S. (2023). Exploring the Sources of Centrality in the Turkish Domestic Airport Network. Journal of Transportation and Logistics, Cilt: 7 - Sayı: 2, 199–212. https://ajindex.com/articles/879904

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (9)

  1. 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
  2. Cats, O. and Krishnakumari, P. (2020),
  3. 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
  4. 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.
  5. 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
  6. 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.
  7. Freeman, L. C. (1978). Centrality in Social Networks Conceptual Clarification. Social Networks, 1, 215–239.
  8. 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
  9. Kılıç Depren, S. and Gökalp Yavuz, F. (2018), The Network Analysis of the Domestic and International Air Transportation Structure of Turk