The impact of the COVID-19 pandemic on O-D flow and airport networks in the origin country and in Northeast Asia

Pei-Fen Kuo; I Gede Brawiswa Putra; Faizal Azmi Setiawan; Tzai-Hung Wen; Chui-Sheng Chiu; Umroh Dian Sulistyah

doi:10.1016/j.jairtraman.2022.102192

The impact of the COVID-19 pandemic on O-D flow and airport networks in the origin country and in Northeast Asia

J Air Transp Manag. 2022 May:100:102192. doi: 10.1016/j.jairtraman.2022.102192. Epub 2022 Feb 17.

Authors

Pei-Fen Kuo¹, I Gede Brawiswa Putra¹, Faizal Azmi Setiawan¹, Tzai-Hung Wen², Chui-Sheng Chiu¹, Umroh Dian Sulistyah¹

Affiliations

¹ Department of Geomatics, National Cheng Kung University, Taiwan.
² Department of Geography, National Taiwan University, Taiwan.

Abstract

The ongoing COVID-19 pandemic has posed a global threat to human health. In order to prevent the spread of this virus, many countries have imposed travel restrictions. This difficult situation has dramatically affected the airline industry by reducing the passenger volume, number of flights, airline flow patterns, and even has changed the entire airport network, especially in Northeast Asia (because it includes the original disease seed). However, although most scholars have used conventional statistical analysis to describe the changes in passenger volume before and during the COVID-19 outbreak, very few of them have applied statistical assessment or time series analysis, and have not even examined how the impact may be different from place to place. Therefore, the purpose of this study was to identify the impact of COVID-19 on the airline industry and affected areas (including the origin-destination flow and the airport network). First, a Clustering Large Applications (CLARA) algorithm was used to group numerous origin-destination (O-D) flow patterns based on their characteristics and to determine if these characteristics have changed the severity of the impact of each cluster during the COVID-19 outbreak. Second, two statistical tests (the paired t-test and the Wilcoxon signed-rank test) were utilized to determine if the entire airport network and the top 30 hub airports changed during COVID-19. Four centrality measurement indices (degree, closeness, eigenvector, and betweenness centrality) of the airports were used to assess the entire network and ranking of individual hub airports. The study data, provided by The Official Aviation Guide (OAG) from December 2019 to April 2020, indicated that during the COVID-19 outbreak, there was a decrease in passenger volume (60%-98.4%) as well as the number of flights (1.5%-82.6%). However, there were no such significant changes regarding the popularity ranking of most airports during the outbreak. Before this occurred (December 2019), most hub airports were in China (April 2020), and this trend remain similar during the COVID-19 outbreak. However, the values of the centrality measurement decreased significantly for most hub airports due to travel restrictions issued by the government.

Keywords: Airport network; CLARA clustering Algorithm; COVID-19; Centrality.