Development cost prediction of general aviation aircraft using combined estimation technique

Obtaining accurate development cost estimation results of general aviation aircraft is crucial for companies to adopt the best strategy in the development process.To address this problem,this paper proposes a combination of three commonly used single prediction methods.The optimal weight values of the three single prediction methods are determined by utilizing the shortest ideal point method.Ten cost datasets collected from literature are utilized for fitting and testing the combined prediction method,and the weight coefficients of the three individual prediction methods are calculated as 0.6859,0.0035 and 0.3106,respectively.The results of this study indicate that the developed method has better fitting and estimation accuracy than that of the three individual methods,with average fitting and predicting error values of 2.60%and 6.43%,respectively.Additionally,the cost data of military and civil aircraft development from literature are collected for verification.The results further confirm that the proposed method is not only superior to the single prediction methods in terms of high precision but has wider applications.More importantly,this research can provide important reference for general aviation aircraft companies in term of product cost planning and corporate sales strategies.

A Meta-Learning Approach for Aircraft Trajectory Prediction

The aviation industry has seen significant advancements in safety procedures over the past few decades, resulting in a steady decline in aviation deaths worldwide. However, the safety standards in General Aviation (GA) are still lower compared to those in commercial aviation. With the anticipated growth in air travel, there is an imminent need to improve operational safety in GA. One way to improve aircraft and operational safety is through trajectory prediction. Trajectory prediction plays a key role in optimizing air traffic control and improving overall flight safety. This paper proposes a meta-learning approach to predict short- to mid-term trajectories of aircraft using historical real flight data collected from multiple GA aircraft. The proposed solution brings together multiple models to improve prediction accuracy. In this paper, we are combining two models, Random Forest Regression (RFR) and Long Short-term Memory (LSTM), using k-Nearest Neighbors (k-NN), to output the final prediction based on the combined output of the individual models. This approach gives our model an edge over single-model predictions. We present the results of our meta-learner and evaluate its performance against individual models using the Mean Absolute Error (MAE), Absolute Altitude Error (AAE), and Root Mean Squared Error (RMSE) evaluation metrics. The proposed methodology for aircraft trajectory forecasting is discussed in detail, accompanied by a literature review and an overview of the data preprocessing techniques used. The results demonstrate that the proposed meta-learner outperforms individual models in terms of accuracy, providing a more robust and proactive approach to improve operational safety in GA.