  The use of neural networks to predict airport passenger activity choices
inside the terminal is presented in this paper. Three network architectures are
proposed: Feedforward Neural Networks (FNN), Long Short-Term Memory (LSTM)
networks, and a combination of the two. Inputs to these models are both static
(passenger and trip characteristics) and dynamic (real-time passenger
tracking). A real-world case study exemplifies the application of these models,
using anonymous WiFi traces collected at Bologna Airport to train the networks.
The performance of the models were evaluated according to the misclassification
rate of passenger activity choices. In the LSTM approach, two different
multi-step forecasting strategies are tested. According to our findings, the
direct LSTM approach provides better results than the FNN, especially when the
prediction horizon is relatively short (20 minutes or less).
