Analysis of Guarulhos Point Merge Adherence Based on Additional Time in TMA and Trajectory Clustering

• Bruno Monteiro Franzini
• Bernardo Hoffmann Silva
• Victor Maciel
• Tábada Cesar Domingues Silva
• Alef Gonçalves Quintanilha
• Ivan Matias da Silva
• Pollyanne Evangelista da Silva
• André Elias Melo
• Carlos H. Q. Forster
• Jean Phelipe de Oliveira Lima

Airspace Control Institute (ICEA), Research Division · Aeronautics Institute of Technology (ITA)

Evento

XXI SITRAER – Fortaleza, CE, Brasil, 2024

Volume

Anais do XXI Simpósio de Transporte Aéreo – SITRAER 2024 · ISBN 978-65-272-1008-5 · DOI 10.29327/xxi-sitraer-2024

Páginas

290–299

Editora

Even3 Publicações

Editores

Francisco Heber Lacerda de Oliveira, Fabiano Rocha, Giovanna Miceli Ronzani Borille, Evandro José da Silva, Mauro Caetano

Abstract

The increasing global air traffic requires efficient management systems. One way to achieve that is optimizing arrival sequences, reducing flight duration, and enhancing operational safety. A method to address these requirements is a sequencing technique called Point Merge system (PM). This study analyzes the adherence to the PM at Guarulhos International Airport (GRU), aiming to understand the peak and off-peak usage times, and provide a detailed sector-by-sector analysis. Two methods were applied for trajectories classification according to their adherence to the PM: A method based on the 8th ICAO’s Key Performance Indicator (KPI08-based method), which consists of thresholding based on additional time in terminal area, and clustering by the agglomerative method using trajectories data. The experimental data were collected from: KPI08 dataset, airport movements and radar synthesis, focusing on medium-sized commercial aircraft. The total number of flights analyzed was 2427. The results obtained indicate a better performance of the KPI08-based method, which achieved a more defined representation between the classified trajectory groups. The results highlighted a behavior pattern in relation to peak and off-peak times in the use of PM in all periods analyzed. Evaluating the North and West sectors, it was possible to show that some time slots exhibited similar behaviors when using the KPI08-based method. The time slots 8h-9h and 18h-19h stand out as they show that more than 70% of flights execute the PM. The time slot 20h-22h showed similar behavior in both sectors, with approximately 50% of flights executing the PM. This work contributes by proposing the KPI08-based method, an innovative method to classify flights according to the adherence to the PM, which proved to be more accurate than the agglomerative trajectory clustering method. Additionally, this study may indicate an improvement in the arrival flow management at GRU, due to demand predictability. This predictability enables better route planning, which may result in additional fuel reduction.

Keywords: Air Traffic Flow Management; Additional Time in TMA; Point Merge; Trajectory Clustering; Agglomerative clustering