How influential factors affect aviation networks: A Bayesian network analysis

The aviation industry was the catalyst for the economic development of advanced cities, and there has been a close relationship between the development of cities' aviation industries and economic indexes. Researchers have long been interested in investigating which economic factors affect the passenger and cargo volumes of airports. By leveraging statistical analysis, most existing research only indicates the significant factors affecting aviation networks and quantifies the positive or negative relationship between those factors and aviation passenger volume. However, it is difficult to envisage how the degree of changes in economic factors affects aviation networks, especially passenger and cargo volume. This paper utilizes Bayesian network analysis to bridge this gap. The airport-level data collected from OAG was combined with city- or country-level economic data that are exploited to build the Bayesian network. We find that GDP and inflation directly influence passenger and cargo volume, while fuel prices directly influence only cargo volume. Both networks change with time, indicating that evolving external economic factors influence the network. This study is the pioneer in using Bayesian network analysis to analyze aviation networks. We identify how airport passenger and cargo volumes change with respect to different degrees of economic factors change. In addition, the Bayesian network exhibits the output in a probabilistic way to fully address the uncertainty worldwide. The findings could potentially facilitate policymakers’ decisions to improve global aviation network development. Copyright © 2020 Elsevier Ltd. All rights reserved.