Abstract
By utilizing the theory of complex network, we investigate the structural features of China’s passenger ATNs (Air Transport Networks) from the perspective of multi-layer networks. The results indicate that with the superposition of the ATNs of four major airline groups, the aggregated ATNs will gradually present the features of high clustering coefficient, short average path length and high average degree. We also find that when the number of node classes in the aggregation ATNs changes, the variation range of the structural properties of the aggregate ATNs exhibit the different behaviors in the process of superposition. Finally, by adding other small airlines into the above-mentioned aggregated ATNs, it is found that the high clustering coefficient of China’s ATNs and the uneven load are mainly determined by the ATN of four major airline groups, but the average degree of the network is greatly affected by the small airlines ATN. The current findings are beneficial for us to understand the structural properties of Chinese ATNs and further improve the design of domestic flight routs in the future.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Colizza V, Barrat A, Barthélemy M et al (2006) The role of the airline transportation network in the prediction and predictability of global epidemics. Proc Natl Acad Sci USA 103(7):2015–2020
Guimera R, Amaral LAN (2004) Modeling the world-wide airport network. Eur Phys J B 38(2):381–385
Guimera R, Mossa S, Turtschi A et al (2005) The worldwide air transportation network: anomalous centrality, community structure, and cities’ global roles. Proc Natl Acad Sci USA 102(22):7794–7799
Hsu C, Shih H (2008) Small-world network theory in the study of network connectivity and efficiency of complementary international airline alliances. J Air Transp Manag 14(3):123–129
Paleari S, Redondi R, Malighetti P (2010) A comparative study of airport connectivity in China, Europe and US: which network provides the best service to passengers? Transp Res Part E Logist Transp Rev 46(2):198–210
Barrat A, Barthélemy M, Vespignani A (2005) The effects of spatial constraints on the evolution of weighted complex networks. J Stat Mech Theory Exp 2005(05):P05003
Xu Z, Harriss R (2008) Exploring the structure of the US intercity passenger air transportation network: a weighted complex network approach. GeoJournal 73(2):87
Liu H, Tao Z (2007) Empirical study of Chinese city airline network. Acta Phys Sinica 56(1):106–112
Hong C, Zhang J, Cao X et al (2016) Structural properties of the Chinese air transportation multilayer network. Chaos, Solitons Fractals 86:28–34
Wei L, Cai X (2004) Statistical analysis of airport network of China. Phys Rev E 69(4):046106
Lin J (2012) Network analysis of China’s aviation system, statistical and spatial structure. J Transp Geogr 22:109–117
Liu H, Zhang X, Lang C et al (2009) Analysis on the connecting mechanism of Chinese city airline network. Sci China Ser G Phys Mech Astron 39(7):935–942
Peng Y, Zhou Y (2009) Network structure of air transport in China. Econ Geogr 29:1850–1854
Wang J, Mo H (2014) Complex evolution process of China’s air transport network. J Transp Syst Eng Inf Technol 14(1):71–80
Zeng X, Tang X, Jiang K (2011) Empirical study of Chinese airline network structure based on complex network theory. J Transp Syst Eng Inf Technol 11(6):175–181
Zhang Y, Round DK (2009) Policy implications of the effects of concentration and multimarket contact in China’s airline market. Rev Ind Organ 34(4):307–326
Bagler G (2008) Analysis of the airport network of India as a complex weighted network. Phys A 387(12):2972–2980
Da Rocha LEC (2009) Structural evolution of the Brazilian airport network. J Stat Mech Theory Exp 2009(04):P04020
Cardillo A, Zanin M, Gómez-Gardenes J et al (2013) Modeling the multi-layer nature of the European air transport network: resilience and passengers re-scheduling under random failures. Eur Phys J Spec Top 215(1):23–33
Verma T, Araújo NAM, Herrmann HJ (2014) Revealing the structure of the world airline network. Sci Rep 4:5638
Wang P, Hunter T, Bayen AM et al (2012) Understanding road usage patterns in urban areas. Sci Rep 2(12):1001
Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512
Newman ME (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256
Battiston F, Nicosia V, Latora V (2014) Structural measures for multiplex networks. Phys Rev E 89(3):032804
Costa LF, Rodrigues FA, Travieso G et al (2007) Characterization of complex networks: a survey of measurements. Adv Phys 56(1):167–242
Boccaletti S, Bianconi G, Herrero RC et al (2014) The structure and dynamics of multilayer networks. Phys Rep 544(1):1–122
Acknowledgments
This work was supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 61773286, and Tianjin Municipal Natural Science Foundation under grant no. 18JCYBJC87800.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, H., Sun, S., Wang, L., Xia, C. (2020). Structural Analyses of Chinese Passenger Airline Network from the Perspective of Multi-layer Networks. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 593. Springer, Singapore. https://doi.org/10.1007/978-981-32-9686-2_47
Download citation
DOI: https://doi.org/10.1007/978-981-32-9686-2_47
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-32-9685-5
Online ISBN: 978-981-32-9686-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)