Neural networks trained with WiFi traces to predict airport passenger behavior

TL;DR

This paper explores neural network models, including FNN and LSTM, trained on WiFi data to accurately predict passenger behavior inside an airport terminal, demonstrating improved short-term prediction performance.

Contribution

It introduces a combined neural network approach using WiFi traces for real-time passenger activity prediction at airports, with comparative analysis of different architectures.

Findings

LSTM outperforms FNN in short-term predictions

Direct multi-step LSTM approach yields better accuracy

WiFi data effectively trains models for passenger behavior prediction

Abstract

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).