Identification model development for proactive response on irregular operations (IROPs)

Abstract The demand for air transportation service in Korea has been increasing rapidly, while the airport operation system has become even further advanced and complex. Accordingly, concerns regarding negative ripple effects, such as damage caused by flight delays or cancellations due to irregular operations (IROPs) of airports, have been amplifying. The IROPs is being newly defined, and guidelines for establishing a response system are being proposed in the U.S. and the U.K. However, studies in relation to preemptive and predictive responses aimed at minimizing the negative impacts, such as to analyze ripple effects generated after an incident, have not been sufficiently conducted. Accordingly, this study was conducted to analyze the ripple effects of IROPs according to severity and duration time, and to thus suggest a methodology to enable predictive response. The situation of IROPs was simulated and analyzed using the tower log data of 2015 from Jeju International Airport (CJU), Gimpo International Airport (GMP), and Gimhae International Airport (PUS) in Korea. The five-level classification for IROPs was then suggested using the K-means algorithm. The methodology suggested was verified for applicability to actual airport operation through scenario analysis. It is expected to serve as a framework for establishing the quantitative standards for goal setting with which airport operators solve the situation of IROPs.