Towards Analysing the Impact of Go-Around Occurrences at Large European Airports
Keywords:Go-around, Safety, Airport Performance, Automatic Dependent Surveillance-Broadcast
Go-arounds (GoA) or missed approaches are standard flight procedures initiated when an approach is aborted for safety reasons, requiring pilots to reposition the aircraft for a subsequent landing attempt. This study leverages ADS-B data sourced from the OpenSky Network, collected at 20 major European airports between January 2019 and July 2023. Out of 6.7 million retrieved landing trajectories, 20,196 GoA were identified and analyzed.
We conducted statistical evaluations on these GoA instances to compare the rates of GoA at different airports, market segments, and aircraft types. We also looked at the distributions of distance, duration, and fuel consumption of GoA for the different airports.
Of particular note, we quantified the impact of a GoA on the surrounding arrival traffic by analyzing how GoA events affect Arrival Sequencing and Metering Area (ASMA) timings.
Our results show that the rate of GoA at the assessed airports ranged from 1.5 to 6 occurrences per 1,000 landings. The median duration and distance of a GoA varied depending on the airport, falling between 11.5 and 16.5 minutes, and 36.5 and 58.2 NM respectively. During a GoA, an Airbus A320 typically consumes between 350 and 600 kg of fuel.
Importantly, our findings demonstrate that a GoA occurrence can significantly impact the efficiency of arriving traffic at an airport, causing disruptions lasting up to an hour. ASMA timings tend to increase directly after a GoA occurs and peak for landings occurring 10 to 25 minutes after a GoA, with flight time increases ranging from 30 to 100 seconds, depending on the airport. These timings gradually return to their pre-GoA levels within the following hour, though certain airports may experience longer recovery periods.
This comprehensive study on Go-arounds (GoA) provides a deeper understanding of their impact, offering valuable insights that can support data-driven decision-making in the aviation industry.
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Copyright (c) 2023 Benoit Figuet, Esther Calvo Fernández, Rainer Koelle, Manuel Waltert
This work is licensed under a Creative Commons Attribution 4.0 International License.