Predicting Passenger Flow at Charles De Gaulle Airport using Dense Neural Networks

Daniel Delahaye (1) , Alexis Brun (1) , Sameer Alam (2) , Eric Feron (3)

1 OPTIM - ENAC - Equipe Optim
2 NTU - Nanayang Technological University
3 KAUST - King Abdullah University of Science and Technology [Saudi Arabia]

Security checking is a major issue in airport operations. Affecting the correct number of security agents is essential to provide a good quality of service to passengers while providing the best security performances. At Paris Charles de Gaulle airport the affectation of security agents is decided at strategical level, more than a month in advance. The key element to determine the number of agents needed is the passenger flow through the security checkpoints. This flow is correlated to the passenger flow in the different boarding rooms. This paper investigates the interest of small dense neural networks to perform passenger flow prediction at strategical level for Paris Charles de Gaulle airport. A dense neural network has been trained to predict the passenger flow for each boarding room of the airport. The network has been compared to a more complex long short-term memory model in terms of mean absolute error and outperformed a mathematical model based on exponentially modified Gaussian distribution.

Mots clés: Airport Operations; Passenger Flow Prediction; Dense Neural Network; Strategic Prediction

Domaines: Intelligence artificielle [cs.AI]

Daniel Delahaye, Alexis Brun, Sameer Alam, Eric Feron. Predicting Passenger Flow at Charles De Gaulle Airport using Dense Neural Networks. International Workshop on ATM/CNS (IWAC) 2022, Oct 2022, Tokyo, Japan. ⟨hal-03852025⟩