Characterizing the Brazilian airspace structure and air traffic performance via trajectory data analytics

Murça, Mayara Condé Rocha; Guterres, Marcelo Xavier; Oliveira, McWillian de; Szenczuk, João Basílio Tarelho; Souza, Wallace S. S.

doi:10.1016/j.jairtraman.2020.101798

Cited by 18 publications

(6 citation statements)

References 7 publications

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“…M. Conde Rocha Murca et al [30] developed an air traffic flow characterization framework composed of three sequential modules. The first module uses DBSACN to learn typical patterns of operation from radar tracks [31]. Built on this knowledge, the second module uses random forests to identify non-conforming trajectories.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Trajectory Clustering in Terminal Airspace Based on Deep Autoencoder and Gaussian Mixture Model

Zeng

Cai

et al. 2021

Aerospace

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The aircraft trajectory clustering analysis in the terminal airspace is conducive to determining the representative route structure of the arrival and departure trajectory and extracting their typical patterns, which is important for air traffic management such as airspace structure optimization, trajectory planning, and trajectory prediction. However, the current clustering methods perform poorly due to the large flight traffic, high density, and complex airspace structure in the terminal airspace. In recent years, the continuous development of Deep Learning has demonstrated its powerful ability to extract internal potential features of large dataset. Therefore, this paper mainly tries a deep trajectory clustering method based on deep autoencoder (DAE). To this end, this paper proposes a trajectory clustering method based on deep autoencoder (DAE) and Gaussian mixture model (GMM) to mine the prevailing traffic flow patterns in the terminal airspace. The DAE is trained to extract feature representations from historical high-dimensional trajectory data. Subsequently, the output of DAE is input into GMM for clustering. This paper takes the terminal airspace of Guangzhou Baiyun International Airport in China as a case to verify the proposed method. Through the direct visualization and dimensionality reduction visualization of the clustering results, it is found that the traffic flow patterns identified by the clustering method in this paper are intuitive and separable.

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Section: Introductionmentioning

confidence: 99%

Aircraft Trajectory Clustering in Terminal Airspace Based on Deep Autoencoder and Gaussian Mixture Model

Zeng

Cai

et al. 2021

Aerospace

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“…Our methodology for network construction is inspired by the recent work of Murça et al (2020) , who use trajectory clustering for the purpose of pattern identification in the Brazilian air transportation system. Specifically, we apply an unsupervised machine learning methods with the aim to identify groups of highly similar trajectories without any predefined information on the airspace structure.…”

Section: Problem Formulation and Modelmentioning

confidence: 99%

On the ramifications of airspace bans in aero-political conflicts: Towards a country importance ranking

2023

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“…The intent of the DBSCAN clustering in our work is to assign trajectories to clusters in an unsupervised way similar to [50], [82], thus identifying and separating the regular mission trajectories within the noise of recreational hobbyist We also evaluated the percentages of trajectory points per cluster for Scenario-0 as shown in FIGURE 10 below. It is evident that most of the trajectories detected are of the COVID-19 sample delivery missions as shuttle service with 56% weightage compared with delivery package services that constitute 44% of the trajectories of missions.…”

Section: ) Scenario 0 -Ideal Case Without Nfz Weather Constraints And...mentioning

confidence: 99%

Modeling and Characterization of Traffic Flow Patterns and Identification of Airspace Density for UTM Application

2022

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Current airspace has limited resources, and the widespread use of unmanned aerial vehicles (UAVs) increases airspace density, which is already crowded with manned aircraft. This demands the improvement of airspace safety and capacity while considering all parametric uncertainties that may hinder aircraft and UAV mobility such as dynamic airspace structures and weather conditions. This paper proposes a data analytics framework to characterize traffic flow patterns of unmanned traffic management (UTM) airspace by analyzing simulated historical data. Mission patterns are characterized and identified by considering multiple UAV missions and scenarios with different priority levels to highlight UAVs' trajectories and deviations from the actual path due to these constraints. The pertinent data analysis supports risk analysis and improves trajectory planning in different airspace regions considering all dynamic parameters such as extreme weather, emergency services, and dynamic airspace structures. The data processing framework, which is density-based spatial clustering of applications with noise (DBSCAN), identified significant deviations in mission patterns with almost 82% confidence level. The UTM traffic flow characterization is conducted by three key characterization parameters mainly Distance from Centroid (DFC), Distance to Complete Mission (DTCM) and Time to Complete Mission (TTCM). This work also analyzed the airspace congestion using the Kernel density estimation (KDE). This analysis identified some regions of interference as potential congested areas representing safety concerns. The proposed framework is envisioned to assist UTM authority by characterizing air traffic behavior, managing its flow, improving airspace design, and providing the basis for developing predictive capabilities that support traffic flow management.

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Characterizing the Brazilian airspace structure and air traffic performance via trajectory data analytics

Cited by 18 publications

References 7 publications

Aircraft Trajectory Clustering in Terminal Airspace Based on Deep Autoencoder and Gaussian Mixture Model

Aircraft Trajectory Clustering in Terminal Airspace Based on Deep Autoencoder and Gaussian Mixture Model

On the ramifications of airspace bans in aero-political conflicts: Towards a country importance ranking

Modeling and Characterization of Traffic Flow Patterns and Identification of Airspace Density for UTM Application

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