Towards high-performance global air transportation networks using socioeconomic-environmental data

Abstract

This paper proposes a multi-objective optimization procedure for a global air transportation system to find a balance among risk, economy, and convenience using a “divide and conquer” method while improving each corresponding metric simultaneously. To do so, we attempt to estimate several geographical parameters of airports from socioeconomic-technological-environmental databases. Considering the risk of tsunami run-up events to air transportation, we estimate physical exposures of airports utilized in global aviation networks. First, we evaluate spatial risk of tsunami run-up events, and estimate physical exposure of airports by linking OAG timetable data of international passenger flights, geographical information of airports, tsunami event catalog data, global population estimates of 2.5 minute degree grid square and global earth surface data of 1 minute degree grid square. Second, we estimate regression coefficients of a simple gravity model for a global air transportation system linking OAG timetable data of international passenger flights, geographical information of airports, and global population estimates of 2.5 minute degree grid square. Finally, imposing three types of objective functions, economy, convenience, and risks, we formalize a multi-objective optimization problem to design efficient routes for a global aviation network. These objective functions are functions in terms of physical exposures and the number of passengers from a departure airport to an arrival airport for all possible combinations. However, because the global aviation network is too large to optimize simultaneously, we use a “divide and conquer” method to tackle the large-scale problem by dividing the whole global air transportation network into each airline company's network and successfully integrating them. We discuss the procedure of collecting relevant databases, analyzing the aviation networks, evaluating the performance metrics, and applying the “divide and conquer” approach to the global transportation network.