  Inspired by the success of deep learning (DL) in natural language processing
(NLP), we applied cutting-edge DL techniques to predict flight departure demand
in a strategic time horizon (4 hours or longer). This work was conducted in
support of a MITRE-developed mobile application, Pacer, which displays
predicted departure demand to general aviation (GA) flight operators so they
can have better situation awareness of the potential for departure delays
during busy periods. Field demonstrations involving Pacer's previously designed
rule-based prediction method showed that the prediction accuracy of departure
demand still has room for improvement. This research strives to improve
prediction accuracy from two key aspects: better data sources and robust
forecasting algorithms. We leveraged two data sources, Aviation System
Performance Metrics (ASPM) and System Wide Information Management (SWIM), as
our input. We then trained forecasting models with DL techniques of sequence to
sequence (seq2seq) and seq2seq with attention. The case study has shown that
our seq2seq with attention performs best among four forecasting algorithms
tested. In addition, with better data sources, seq2seq with attention can
reduce mean squared error (mse) over 60%, compared to the classical
autoregressive (AR) forecasting method.
