Prediction of 3D Airspace Occupancy Using Machine Learning

Cristian Lozano Tafur (), Jaime Orduy Rodríguez, Pedro Melo Daza, Iván Rodríguez Barón, Danny Stevens Traslaviña and Juan Andrés Bermúdez
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Cristian Lozano Tafur: Department of Engineering, Fundación Universitaria Los Libertadores, Bogotá 111221440, Colombia
Jaime Orduy Rodríguez: Department of Engineering, Fundación Universitaria Los Libertadores, Bogotá 111221440, Colombia
Pedro Melo Daza: Department of Engineering, Fundación Universitaria Los Libertadores, Bogotá 111221440, Colombia
Iván Rodríguez Barón: Department of Engineering, Fundación Universitaria Los Libertadores, Bogotá 111221440, Colombia
Danny Stevens Traslaviña: Department of Engineering, Fundación Universitaria Los Libertadores, Bogotá 111221440, Colombia
Juan Andrés Bermúdez: Escuela de Aviación del Ejército, Bogotá 110911, Colombia

Forecasting, 2025, vol. 7, issue 4, 1-39

Abstract: This research introduces a system designed to predict three-dimensional airspace occupancy over Colombia using historical Automatic Dependent Surveillance-Broadcast (ADS-B) data and machine learning techniques. The goal is to support proactive air traffic management by estimating future aircraft positions—specifically their latitude, longitude, and flight level. To achieve this, four predictive models were developed and tested: K-Nearest Neighbors (KNN), Random Forest, Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM). Among them, the LSTM model delivered the most accurate results, with a Mean Absolute Error (MAE) of 312.59, a Root Mean Squared Error (RMSE) of 1187.43, and a coefficient of determination (R 2 ) of 0.7523. Compared to the baseline models (KNN, Random Forest, XGBoost), these values represent an improvement of approximately 91% in MAE, 83% in RMSE, and an eighteen-fold increase in R 2 , demonstrating the substantial advantage of the LSTM approach. These metrics indicate a significant improvement over the other models, particularly in capturing temporal patterns and adjusting to evolving traffic conditions. The strength of the LSTM approach lies in its ability to model sequential data and adapt to dynamic environments—making it especially suitable for supporting future Trajectory-Based Operations (TBO). The results confirm that predicting airspace occupancy in three dimensions using historical data are not only possible but can yield reliable and actionable insights. Looking ahead, the integration of hybrid neural network architectures and their deployment in real-time systems offer promising directions to enhance both accuracy and operational value.

Keywords: trajectory prediction; machine learning; airspace; air traffic management (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2025
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