Airport passenger flow prediction using LTSM Recurrent Neural Networks

Roberto Salvador Felix Patron, Paolo Scala, Miguel Mujica Mota, Alejandro Murrieta Mendoza

Research output: Contribution to conferencePaperAcademic

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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
Number of pages10
Publication statusPublished - 2021
EventAir Transport Research Society World Conference 2021 - Sydney, Australia / Online, Sydney, Australia
Duration: 26 Aug 202129 Aug 2021


ConferenceAir Transport Research Society World Conference 2021
Abbreviated titleATRS 2021
Internet address


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