1. Proceedings
  2. I3M
  3. 2021
  4. EMSS
  5. 10.46354/i3m.2021.emss.023

Airport passenger flow prediction using simulation data farming and machine learning

  • a Roberto Salvador Félix Patrón ,
  • b Paolo Scala, 
  • c Miguel Mújica Mota,
  • d Alejandro Murrieta Mendoza
  • a,c,d  Aviation Academy, Amsterdam University of Applied Sciences, Weesperzijde 190, Amsterdam, 1097DZ, The Netherlands
  • b  Amsterdam School of International Business, Amsterdam University of Applied Sciences, Fraijlemaborg 133, Amsterdam, 1102CV, The Netherlands
Cite as
Patrón R.S.F., Scala P., Mújica Mota M., Murrieta Mendoza A. (2021). Airport passenger flow prediction using simulation data farming and machine learning. Proceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021), pp. 165-172. DOI: https://doi.org/10.46354/i3m.2021.emss.023

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.

References

  1. AENA. (2021). AENA. Retrieved from
    https://portal.aena.es/csee/Satellite?Language=EN_GB&ca=PMI&pagename=cartografia&ps=t&ti=T
  2. Alodhaibi, S., Burdett, R. L., & Yarlagadda, P. K. D. V. (2017). Framework for Airport Outbound Passenger Flow Modelling. Procedia Engineering, 174, 1100-1109. doi:https://doi.org/10.1016/j.proeng.2017.01.263
  3. ARC. (2020). CAST. Retrieved from https://arc.de/cast-simulation-software/
  4. Barry, P., & Koehler, M. (2004, 5-8 Dec. 2004). Simulation in context; using data farming for decision support. Paper presented at the Proceedings of the 2004 Winter Simulation Conference, 2004.
  5. Chollet, F. (2015). Keras. GitHub repository. Retrieved from https://github.com/fchollet/keras
  6. Gardner, M. W., & Dorling, S. R. (1998). Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric Environment, 32(14), 2627-2636. doi:https://doi.org/10.1016/S1352-2310(97)00447-0
  7. Gatersleben, M. R., & Weij, S. W. V. d. (1999, 5-8 Dec. 1999). Analysis and simulation of passenger flows in an airport terminal. Paper presented at the WSC'99. 1999 Winter Simulation Conference Proceedings. 'Simulation - A Bridge to the Future' (Cat. No.99CH37038).
  8. Graves, A. (2013). Generating Sequences With Recurrent Neural Networks. ArXiv, abs/1308.0850. 
  9. Hinton, G. E., Vinyals, O., & Dean, J. (2015). Distilling the Knowledge in a Neural Network. ArXiv, abs/1503.02531. 
  10. Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.1735
  11. Kalakou, S., & Moura, F. (2015). Modelling Passengers’ Activity Choice in Airport Terminal before the Security Checkpoint: The Case of Portela Airport in Lisbon. Transportation Research Procedia, 10, 881-890. doi:https://doi.org/10.1016/j.trpro.2015.09.041
  12. LeNail, A. (2019). NN-SVG: Publication-Ready Neural Network Architecture Schematics. J. Open Source Softw., 4, 747. 
  13. Lipton, Z. C. (2015). A Critical Review of Recurrent Neural Networks for Sequence Learning. ArXiv, abs/1506.00019. 
  14. Liu, L., & Chen, R.-C. (2017). A novel passenger flow prediction model using deep learning methods. Transportation Research Part C: Emerging Technologies, 84, 74-91. doi:https://doi.org/10.1016/j.trc.2017.08.001
  15. Milbredt, O., Castro, A., Ayazkhani, A., & Christ, T. (2017). Passenger-centric airport management via new terminal interior design concepts. Transportation Research Procedia, 27, 1235-1241. doi:https://doi.org/10.1016/j.trpro.2017.12.008
  16. Mujica Mota, M., Scala, P., Herranz, R., Schultz, M., & Jimenez, E. (2020). Creating the future airport passenger experience: IMHOTEP. Paper presented at the Proceedings of the 32nd European Modeling & Simulation Symposium (EMSS 2020).
  17. Nikoue, H., Marzouli, A., Clarke, J. P., Feron, E., & Peters, J. (2015). Passenger Flow Predictions at Sydney International Airport: A Data-Driven Queuing Approach. Retrieved from 
  18. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., . . . Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res., 12(null), 2825–2830. 
    Verma, A., Tahlyan, D., & Bhusari, S. (2020). Agent based simulation model for improving passenger service time at Bangalore airport. Case Studies on Transport Policy, 8(1), 85-93. doi:https://doi.org/10.1016/j.cstp.2018.03.001
  19. Wu, C.-L., & Chen, Y. (2019). Effects of passenger characteristics and terminal layout on airport retail revenue: an agent-based simulation approach. Transportation Planning and Technology, 42(2), 167-186. doi:10.1080/03081060.2019.1565163
  20. Zhao, S.-Z., Ni, T.-H., Wang, Y., & Gao, X.-T. (2011). A new approach to the prediction of passenger flow in a transit system. Computers & Mathematics with Applications, 61(8), 1968-1974. doi:https://doi.org/10.1016/j.camwa.2010.08.023
  21. Zhao, Z., Chen, W., Wu, X., Chen, P. C. Y., & Liu, J. (2017). LSTM network: a deep learning approach for short-term traffic forecast. IET Intelligent Transport Systems, 11(2), 68-75. Retrieved from https://digital-library.theiet.org/content/journals/10.1049/iet-its.2016.0208