ATM/CNSに関する国際ワークショップ予稿集
Online ISSN : 2758-1586
2022 International Workshop on ATM/CNS
会議情報

Predicting Passenger Flow at Charles De Gaulle Airport using Dense Neural Networks
*Alexis BRUNDaniel DELAHAYEEric FERONSameer Sameer
著者情報
会議録・要旨集 フリー

p. 25-32

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抄録
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.
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この記事はクリエイティブ・コモンズ [表示 4.0 国際]ライセンスの下に提供されています。
https://creativecommons.org/licenses/by/4.0/deed.ja
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