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Application of Complex Network Theory and Genetic Algorithm in Airline Route Networks

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Di Paolo, Ezequiel, Zhang, Ke, Yang, Saini, Hu, Xiao-Bing and Liu, Hao (2011) Application of Complex Network Theory and Genetic Algorithm in Airline Route Networks. Transportation Research Record: Journal of the Transportation Research Board, Vol.2214 . pp. 50-58. doi:10.3141/2214-07 ISSN 0361-1981.

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Official URL: http://dx.doi.org/10.3141/2214-07

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Abstract

To cope with increasing customer demand and market changes, airline companies need to organize and manage their route networks in a more cost-efficient way. In addition, the robustness of flight operations against unpredictable accidents such as terrorist attacks and natural disasters has become more important to airlines. In this study, the concepts and techniques from complex network theory are used to model airline route networks, and then an effective and efficient genetic algorithm is developed to optimize airline route networks in terms of network properties that may have crucial roles to play in improving the cost-effectiveness and reliability of airspace systems. The simulation results illustrate that the work reported in this study has a good potential to improve the topology of airline route networks in terms of given network properties such as operating costs and network robustness.

Item Type: Journal Article
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Science, Engineering and Medicine > Engineering > Engineering
Journal or Publication Title: Transportation Research Record: Journal of the Transportation Research Board
Publisher: U.S. National Research Council * Transportation Research Board
ISSN: 0361-1981
Official Date: 2011
Dates:
DateEvent
2011Published
Volume: Vol.2214
Number of Pages: 9
Page Range: pp. 50-58
DOI: 10.3141/2214-07
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Funder: State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, China, Dynamic Traffic Analysis Technologies for Road Network Management Based on Granular Computing

Data sourced from Thomson Reuters' Web of Knowledge

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