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Predicting Passenger Flow at Charles De Gaulle Airport Security Checkpoints

Abstract : Airport security checkpoints are critical areas in airport operations. Airports have to manage an important passenger flow at these checkpoints for security reason while maintaining service quality. The cost and quality of such an activity depend on the human resource management for these security operations. An appropriate human resource management can be obtained using an estimation of the passenger flow. This paper investigates the prediction at a strategic level of the passenger flows at Paris Charles De Gaulle airport security checkpoints using machine learning techniques such as Long Short-Term Memory neural networks. The derived models are compared to the current prediction model using three different mathematical metrics. In addition, operational metrics are also designed to further analyze the performance of the obtained models.
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https://hal-enac.archives-ouvertes.fr/hal-02506611
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Soumis le : jeudi 12 mars 2020 - 14:14:42
Dernière modification le : mardi 19 octobre 2021 - 11:02:54
Archivage à long terme le : : samedi 13 juin 2020 - 15:17:08

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Philippe Monmousseau, Gabriel Jarry, Florian Bertosio, Daniel Delahaye, Marc Houalla. Predicting Passenger Flow at Charles De Gaulle Airport Security Checkpoints. AIDA-AT 2020, 1st International Conference on Artificial Intelligence and Data Analytics for Air Transportation, Feb 2020, Singapore, Singapore. ⟨10.1109/AIDA-AT48540.2020.9049190⟩. ⟨hal-02506611v1⟩

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