Emergence of new global airline networks and distributing loads in star airports

Abstract

We have developed a method of emerging a small-world network, which has the shortest average path length among complex networks known so far, in a self-organizing manner using an ACO (Ant-Colony Optimization)-inspired method. We call it an n-Star network. As one of the real-world applications, we showed the n-Star network could be applied to reorganizing a next generation global airline network, where several star nodes are assigned to the corresponding star cities selected from major cities in the world in advance, and we evaluated the performance of the network using several kinds of network parameters. This method is a hybrid method using a bottom-up and top-down approach. In this study, without selecting any star cities in advance, using a bottom-up method only based on city population, city ranking, and distance between cities, etc., we tried to emerge a self-organizing global airline network by connecting links between important cities. As a result, the latter n-Star network is formed which is different from the former n-Star network, however, it is expected that both n-Star networks will concentrate heavy loads on their respective star airports. We will verify the concentration of load on the star airports through a simulation experiment, and propose an effective method for distributing the load over the whole airline network. © 2013 Springer-Verlag.