Resumen
Accurate aircraft trajectory prediction is fundamental for enhancing air traffic control systems, ensuring a safe and efficient aviation transportation environment. This research presents a detailed study on the efficacy of the Random Forest (RF) methodology for predicting aircraft trajectories. The study compares the RF approach with two established data-driven models, specifically Long Short-Term Memory (LSTM) and Logistic Regression (LR). The investigation utilizes a significant dataset comprising aircraft trajectory time history data, obtained from a UAS-S4 simulator. Experimental results indicate that within a short-term prediction horizon, the RF methodology surpasses both LSTM and LR in trajectory prediction accuracy and also its robustness to overfitting. The research further fine-tunes the performance of the RF methodology by optimizing various hyperparameters, including the number of estimators, features, depth, split, and leaf. Consequently, these results underscore the viability of the RF methodology as a proven alternative to LSTM and LR models for short-term aircraft trajectory prediction.