Analysis of Weather Factors on Aircraft Cancellation using a Multilayer Complex Network.

Area of Science:

• Aviation Meteorology
• Complex Network Analysis
• Transportation Science

Background:

• Flight cancellations are increasing due to abnormal weather, impacting the airline industry.
• Previous studies often simplified the analysis of weather-related flight cancellations.
• A multilayer approach is needed to understand the interplay of factors causing flight disruptions.

Purpose of the Study:

• To apply a multilayer complex network (MCN) method to investigate aircraft cancellations.
• To analyze the impact of aircraft, rainfall, and wind speed on flight cancellations in South Korea.
• To identify critical nodes and understand the dynamics of weather-induced flight disruptions.

Main Methods:

• Utilized a multilayer complex network (MCN) model.
• Incorporated data on aircraft, rainfall, and wind speed for 14 South Korean airports.
• Applied centrality analysis based on information entropy to identify influential nodes.

Main Results:

• Rainfall was found to have a greater impact on flight cancellations than wind speed.
• Jeju Airport was identified as the most influential node due to high aircraft demand.
• The study demonstrated that aircraft cancellation factors are best defined by links within the MCN.

Conclusions:

• The MCN method provides a robust framework for analyzing complex aviation phenomena.
• Understanding the interconnectedness of factors is crucial for mitigating weather-related flight disruptions.
• The methodology is applicable to both aviation and meteorology, offering insights for operational improvements.