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The optimization of aviation networks is of great significance for optimizing the allocation of resources, improving transport efficiency, and enhancing the competitiveness among airline companies. There have been a lot of researches which combine the theory of complex network and the actual situations to analyze the air transportation system. The present work provides a certain theoretical basis for the plan of airline schedule. Firstly, we regard an airport as a node, flight frequency as a link weight, and build a heterogeneous network. Through empirical analysis, we find that the aviation network has small-world and scale-free properties. In addition, considering that the instant network consists of current flights changing over time, time-varying is another important characteristic of aviation network. Also, a spatiotemporal correspondence between the flight frequency and route geometric distance is demonstrated to be τij~rij-C. Secondly, by Monte Carlo simulation, we know that the time-ordered topologies influence the optimal navigation structure and make it different from those from traditional static models. Specially, we can obtain a unique restriction between C and optimal structural exponent α, which unveils a new optimization principle in route design and schedule arrangement. Applying these features to the cost-minimized optimization model, a method to evaluate the optimization of network is proposed, by which we can directly predict the overall optimal distribution of flight distances and corresponding flight frequencies only based on the information about the passenger flow assignment. Thirdly, China aviation network data from 2001 to 2010 are used for empirical study. It is found that the predictions consist with the actual data. Compared with traditional optimization methods, it can simplify the computational complexity, and therefore it takes full advantage of the structural convenience and provides a new perspective for the overall scheduling of air transportation system. In this case, companies are able to estimate route adjustments easily to see whether they are reasonable and analyze the development trend of network to provide suggestions for future optimization.
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Keywords:
- airline /
- time-varying /
- optimization /
- spatial
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[20] Pajevic S, Plenz D 2011 Nat. Phys. 8 1
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[22] Li G, Reis S, Moreira A, Havlin S, Stanley H E, Andrade Jr J 2013 Phys. Rev. E 87 042810
[23] Li Y, Dou F L, Fan Y, Di Z R 2012 Acta Phys. Sin. 61 228902 (in Chinese) [黎勇, 钭斐玲, 樊瑛, 狄增如 2012 物理学报 61 228902]
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[27] Starnini M, Baronchelli A, Barrat A, Pastor-Satorras R 2012 Phys. Rev. E 85 056115
[28] Trajanovski S, Scellato S, Leontiadis I 2012 Phys. Rev. E 85 066105
[29] Chen Q, Qian J H, Zhu L, Han D D 2016 Phys. Rev. E 93 032219
[30] Chen Q, Qian J H, Zhu L, Han D D 2016 J. Appl. Anal. Comput. 6 30
[31] Wojahn O W 2001 Transport Res. E 37 267
[32] Grosche T, Rothlauf F, Heinzl A 2007 J. Air Transp. Manag. 13 175
[33] Qian J H, Han D D 2009 Physica A 388 4248
[34] Jung W S, Wang F, Stanley H E 2008 Europhys. Lett. 81 48005
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[36] Nõmmik A, Kukemelk S 2016 Aviation 20 32
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[1] Brueckner J K 2004 J. Ind. Econ. 52 291
[2] Li F J, Wang L P, Liu Z Y 2007 Comput. Eng. 33 279 (in Chinese) [李福娟, 王鲁平, 刘仲英 2007 计算机工程 33 279]
[3] Zheng X, Yu T 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA) Ottawa, Canada, September 29-30, 2014 pp1135-1137
[4] Dobson G, Lederer P J 1993 Transp. Sci. 27 281
[5] Wang W, Wang C J 2013 Acta Geogr. Sin. 68 762 (in Chinese) [王伟, 王成金 2013 地理学报 68 762]
[6] Gautreau A, Barrat A, Barthelemy M 2009 Proc. Natl. Acad. Sci. USA 106 8847
[7] Qian J H, Han D D, Ma Y G 2011 Acta Phys. Sin. 60 098901 (in Chinese) [钱江海, 韩定定, 马余刚 2011 物理学报 60 098901]
[8] Han D D, Qian J H, Liu J G 2009 Physica A 388 71
[9] Barrat A, Barthelemy M, Pastor-Satorras R, Vespignani A 2004 Proc. Natl. Acad. Sci. USA 101 3747
[10] Guimera R, Mossa S, Turtschi A, Amaral L A N 2005 Proc. Natl. Acad. Sci. USA 102 7794
[11] Liu H K, Zhou T 2007 Acta Phys. Sin. 56 106 (in Chinese) [刘宏鲲, 周涛 2007 物理学报 56 106]
[12] Luo Y Q, Tang J H, Zhao Z L, Zhu Y W, Dong X J 2014 Complex Systems and Complexity Science 11 4 (in Chinese) [罗赟骞, 汤锦辉, 赵钟磊, 朱永文, 董相均 2014 复杂系统与复杂性科学 11 4]
[13] Lordan O, Sallan J M, Simo P 2014 J. Transp. Geogr. 37 112
[14] Moukarzel C F, de Menezes M A 2002 Phys. Rev. E 65 056709
[15] Kosmidis K, Havlin S, Bunde A 2008 Europhys. Lett. 82 48005
[16] Yang H, Nie Y C, Zeng A, Fan Y, Hu Y Q, Di Z R 2010 Europhys. Lett. 89 58002
[17] Kleinberg J M 2000 Nature 406 845
[18] Kleinberg J M 2000 Proceedings of the Thirty-Second Annual ACM Symposium on Theory of Computing Portland, USA, May 21-23, 2000 pp163-170
[19] Boguna M, Krioukov D, Claffy K C 2009 Nat. Phys. 5 74
[20] Pajevic S, Plenz D 2011 Nat. Phys. 8 1
[21] Milo R, Shenorr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U 2002 Science 298 824
[22] Li G, Reis S, Moreira A, Havlin S, Stanley H E, Andrade Jr J 2013 Phys. Rev. E 87 042810
[23] Li Y, Dou F L, Fan Y, Di Z R 2012 Acta Phys. Sin. 61 228902 (in Chinese) [黎勇, 钭斐玲, 樊瑛, 狄增如 2012 物理学报 61 228902]
[24] Gastner M T, Newman M 2006 Phys. Rev. E 74 016117
[25] Holme P, Saramäki J 2012 Phys. Rep. 519 97
[26] Kim H, Anderson R 2012 Phys. Rev. E 85 026107
[27] Starnini M, Baronchelli A, Barrat A, Pastor-Satorras R 2012 Phys. Rev. E 85 056115
[28] Trajanovski S, Scellato S, Leontiadis I 2012 Phys. Rev. E 85 066105
[29] Chen Q, Qian J H, Zhu L, Han D D 2016 Phys. Rev. E 93 032219
[30] Chen Q, Qian J H, Zhu L, Han D D 2016 J. Appl. Anal. Comput. 6 30
[31] Wojahn O W 2001 Transport Res. E 37 267
[32] Grosche T, Rothlauf F, Heinzl A 2007 J. Air Transp. Manag. 13 175
[33] Qian J H, Han D D 2009 Physica A 388 4248
[34] Jung W S, Wang F, Stanley H E 2008 Europhys. Lett. 81 48005
[35] Qian J H, Han D D 2009 Acta Phys. Sin. 58 3028 (in Chinese) [钱江海, 韩定定 2009 物理学报 58 3028]
[36] Nõmmik A, Kukemelk S 2016 Aviation 20 32
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