  The number of daily sUAS operations in uncontrolled low altitude airspace is
expected to reach into the millions in a few years. Therefore, UAS density
prediction has become an emerging and challenging problem. In this paper, a
deep learning-based UAS instantaneous density prediction model is presented.
The model takes two types of data as input: 1) the historical density generated
from the historical data, and 2) the future sUAS mission information. The
architecture of our model contains four components: Historical Density
Formulation module, UAS Mission Translation module, Mission Feature Extraction
module, and Density Map Projection module. The training and testing data are
generated by a python based simulator which is inspired by the multi-agent air
traffic resource usage simulator (MATRUS) framework. The quality of prediction
is measured by the correlation score and the Area Under the Receiver Operating
Characteristics (AUROC) between the predicted value and simulated value. The
experimental results demonstrate outstanding performance of the deep
learning-based UAS density predictor. Compared to the baseline models, for
simplified traffic scenario where no-fly zones and safe distance among sUASs
are not considered, our model improves the prediction accuracy by more than
15.2% and its correlation score reaches 0.947. In a more realistic scenario,
where the no-fly zone avoidance and the safe distance among sUASs are
maintained using A* routing algorithm, our model can still achieve 0.823
correlation score. Meanwhile, the AUROC can reach 0.951 for the hot spot
prediction.
