Airport passenger flow prediction using simulation data farming and machine learning

P.M. Scala, M.A. Mujica Mota, R.S. Felix Patron, A. Murrieta Mendoza

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Abstract

Passenger flow management is an important issue at many airports around the world. There are high concentrations of passengers arriving and leaving the airport in waves of large volumes in short periods, particularly in big hubs. This might cause congestion in some locations depending on the layout of the terminal building. With a combination of real airport data, as well as synthetic data obtained through an airport simulator, a Long Short-Term Memory Recurrent Neural Network has been implemented to predict the possible trajectories that passengers may travel within the airport depending on user-defined passenger profiles. The aim of this research is to improve passenger flow predictability and situational awareness to make a more efficient use of the airport, that could also positively impact communication with public and private land transport operators.
Original languageEnglish
Title of host publicationProceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021)
EditorsMichael Affenzeller, Agostino G. Bruzzone, Emilio Jimenez, Francesco Longo, Antonella Petrillo
PublisherCAL-TEK
Pages165-172
ISBN (Print)9788885741577
DOIs
Publication statusPublished - 2021

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