4D flight trajectory prediction using a hybrid Deep Learning prediction method based on ADS-B technology: a case study of Hartsfield-Jackson Atlanta International Airport(ATL)

TL;DR

This paper introduces a hybrid deep learning model combining CNN and GRU with MC-Dropout for accurate 4D flight trajectory prediction at ATL, effectively handling uncertainties and outperforming existing models.

Contribution

It presents a novel hybrid CNN-GRU model with MC-Dropout for 4D trajectory prediction, improving accuracy by 21% over other deep learning approaches.

Findings

The hybrid CNN-GRU model with MC-Dropout reduces prediction error by 21%.

The model effectively captures spatial-temporal features and uncertainties.

Results outperform traditional 3D CNN and CNN-GRU models.

Abstract

The core of any flight schedule is the trajectories. In particular, 4D trajectories are the most crucial component for flight attribute prediction. In particular, 4D trajectories are the most crucial component for flight attribute prediction. Each trajectory contains spatial and temporal features that are associated with uncertainties that make the prediction process complex. Today because of the increasing demand for air transportation, it is compulsory for airports and airlines to have an optimized schedule to use all of the airport's infrastructure potential. This is possible using advanced trajectory prediction methods. This paper proposes a novel hybrid deep learning model to extract the spatial and temporal features considering the uncertainty of the prediction model for Hartsfield-Jackson Atlanta International Airport(ATL). Automatic Dependent Surveillance-Broadcast (ADS-B) data are used as input to the models. This research is conducted in three steps: (a) data preprocessing; (b) prediction by a hybrid Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) along with a 3D-CNN model; (c) The third and last step is the comparison of the model's performance with the proposed model by comparing the experimental results. The deep model uncertainty is considered using the Mont-Carlo dropout (MC-Dropout). Mont-Carlo dropouts are added to the network layers to enhance the model's prediction performance by a robust approach of switching off between different neurons. The results show that the proposed model has low error measurements compared to the other models (i.e., 3D CNN, CNN-GRU). The model with MC-dropout reduces the error further by an average of 21 %.