## 4 Results and Discussion  
The database used in this study for testing and validation of the proposed methodology comprised a total of 1820 trajectories and was composed of 218,400 samples. In which, each sample is a vector representing the GPS data (i.e., \(\left[latitude, longitude, altitude, heading, speed, time\right]_{6\times 1}\)) [54]. For enhancing the stability of the GAN model, Initialization of the weight vector and batch normalization were undertaken. \"Z-score normalization\" and \"Batch normalization\" are common methods used to normalize latent vectors. We utilized batch normalization methodology as it has faster convergence. For the proposed generative adversarial model, batch normalization aims to normalize the activations of neurons in a network layer by adjusting them based on the statistics of the current batch of data during  
Figure 6: The structure governing Consensus Decision, Trajectory Storage, and the Execution of New Blocks. training. While batch normalization is commonly used in the layers of a neural network, it is not typically applied directly to latent vectors. Furthermore, a regularization process was implemented to reduce the magnitude of the regressed values. \(L2\) regularization methodology was utilized, in which it added a penalty term \(\lambda\|w\|_{2}^{2}\) to the loss function, and encouraged the weights to be small, and eventually to avoid overfitting. \(\lambda\) denotes regularization strength hyperparameter for parameter vector \(w\). Fully connected Deep Neural Networks (DNN) architecture, with 12 hidden layers and 12 neurons in each one activated by _ReLU_ (Rectified Linear Unit) functions, were utilized for both Generator and Discriminator. In order to make a trade-off between the conservatism and sensitivity of the GAN model, the prediction threshold was set to 0.8. As the GAN model was further designed and developed for trajectory prediction, its learning curves must be evaluated. The GAN should be trained such that the prediction model avoids both overfitting and underfitting. For this purpose, training curves were plotted to show the variation of the Mean Square Error (MSE) loss versus the number of training epoch. Figure 7 shows GAN's training and validation performance in terms of MSE loss variation with the number of training epoch. As shown in Figure 7, the GAN model was well-trained, as the MSE loss decreased over the epochs without abrupt fluctuations. After four epochs, MSE loss curves related to the training and validation performance stabilized and converged to the point of stability with a very small difference. Given the small difference between the learning curves, it can be inferred that the GAN model provided a very good generalization for future trajectory prediction. The other important metric related to the trained GAN model's efficiency is its Discriminator output behaviour during divergence minimization. Figure 8 illustrates the Discriminator's output for the UAS-S4's real and fake trajectory data. As shown in Figure 8, the GAN-generated trajectory data has the aim of converging the Discriminator's output at 0.5. It could consistently minimize the UAS-S4 trajectory divergences corresponding to the real and fake data, showing that the discriminator assigned the same probability of a fake or a real trajectory existence. The Receiver Operating Characteristic (ROC) curve was also considered for the performance analysis. The ROC curve indicates the prediction model parameters, namely, the true and false-positive predicted trajectory rates. A ROC curve serves as a performance index when various thresholds are considered. The area under the ROC curve helps to assess a trained prediction model's efficiency. Figure 9 shows the ROC performance curves related to each of the three methodologies. Figure 7: GAN’s training and validation performance. An ROC is a probability curve, so that the area under it measures the trajectory separation. It reveals to what extent the trajectory prediction model can distinguish between inbound and outbound trajectories.