Modeling and Managing Airport Passenger Flow Under Uncertainty: A Case of Fukuoka Airport in Japan

  • Hiroaki Yamada
  • Kotaro Ohori
  • Tadashige Iwao
  • Akifumi Kira
  • Naoyuki Kamiyama
  • Hiroaki Yoshida
  • Hirokazu Anai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10540)


Airport terminal decision makers in recent years need to deal with unexpected and sudden congestion situations. Although various types of mathematical researchs has analyzed the congestion situations and have succeed to manage a subsystem, they cannot sufficiently describe the variety of phenomena observed in a real airport terminal, because they have not considered the interactions between subsystems of the real airport terminal. A simulation approach enables us to describe the interactions between facilities and passenger behavior in detail as a whole airport system and to find various types of possible congestion situations. The simulation approach, however, cannot directly lead exact prediction that can be useful in practical management and operation for difficulties of modeling a complex airport terminal system and acquiring complete input data. In this paper, (1) we modeled Fukuoka airport international terminal in Japan as Complex Adaptive System and built a passenger flow simulation based on the Discrete Event Model. Validity of the model was confirmed by experiments. Moreover, (2) we confirmed that it is possible to acquire simulation input data from discussing with stakeholders using the simulation. Therefore, we believe that it is possible to reduce uncertainty of the model systematically by continuing modeling, predicting, and discussing with stakeholders, repeatedly.


Passenger flow simulation Airport terminal Complex adaptive system Discrete event model System design methodology 



We appreciate to Fukuoka Airport Building Co., Ltd. for useful comments and considerable efforts. We are grateful to Mr. Daisuke Sunada and Mr. Kazuhiro Tokiwa for developing a simulator implemented our model. Naoyuki Kamiyama was supported by JST PRESTO Grant Number JPMJPR14E1, Japan. Akifumi Kira was supported in part by JSPS KAKENHI Grant Numbers 26730010 and 17K12644.


  1. 1.
    Barbo, W.A.: The use of queuing models in design of baggage claim areas at Airports. Graduate Report. Institute of Transportation and Traffic Engineering, University of California, Berkeley (1967)Google Scholar
  2. 2.
    Beria, P., Laurino, A.: Determinants of daily fluctuations in air passenger volumes. The effect of events and holidays on Milan Malpensa airport. J. Air Transp. Manag. 53, 73–84 (2016)CrossRefGoogle Scholar
  3. 3.
    Borshchev, A., Filippov, A.: From system dynamics and discrete event to practical agent-based modeling: reasons, techniques, tools. In: Proceedings of the 22nd International Conference of the System Dynamics Society, Oxford, England (2004)Google Scholar
  4. 4.
    Bouarfa, S., Blom, H.A., Curran, R., Everdij, M.H.: Agent-based modeling and simulation of emergent behavior in air transportation. Complex Adapt. Syst. Model. 1(15), 1–26 (2013)Google Scholar
  5. 5.
    Cook, A., Blom, H.A.P., Lillo, F., Mantegna, R.N., Miccichè, S., Rivas, D., Zanin, M.: Applying complexity science to air traffic management. J. Air Transp. Manag. 42, 149–158 (2015)CrossRefGoogle Scholar
  6. 6.
    Correia, A.R., Wirasinghe, S.C., de Barros, A.G.: Overall level of service measures for airport passenger terminals. Transp. Res. Part A: Policy Pract. 42(2), 330–346 (2008)Google Scholar
  7. 7.
    Dunlay, W.J., Park, C.H.: Tandem-queue algorithm for airport user flows. Transp. Eng. J. ASCE 104(TE2), 131–149 (1978)Google Scholar
  8. 8.
    Eilon, S., Mathewson, S.: A simulation study for the design of an air terminal building. IEEE Trans. Syst. Man Cybern. 3(4), 308–317 (1973)CrossRefGoogle Scholar
  9. 9.
    Eurocontrol: Impact Study of Landside Elements on Airport Capacity and Delays (2009)Google Scholar
  10. 10.
    Fayez, M.S., Kaylani, A., Cope, D., Rychlik, N., Mollaghasemi, M.: Managing airport operations using simulation. J. Simul. 2, 41–52 (2008)CrossRefGoogle Scholar
  11. 11.
    Gillen, D., Hasheminia, H.: Estimating the demand responses for different sizes of air passenger groups. Transp. Res. Part B: Methodological 49, 24–38 (2013)CrossRefGoogle Scholar
  12. 12.
    Gongora, M., Ashfaq, W.: Analysis of passenger movement at birmingham international airport using evolutionary techniques. In: IEEE Congresson Evolutionary Computation (CEC), pp. 1339–1345 (2006)Google Scholar
  13. 13.
    Heidt, A., Gluchshenko, O.: From uncertainty to robustness and system’s resilience in ATM: a case study. In: Proceedings of the Third International Air Transport and Operations Symposium, Delft, Netherlands (2012)Google Scholar
  14. 14.
    Holland, J.H.: Studying complex adaptive systems. J. Syst. Sci. Complexity 19, 1–8 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Horonjeff, R.: Planning and Design of Airports, 1st edn. McGraw Hill Book Company, New York (1962)Google Scholar
  16. 16.
    Jim, H.K., Chang, Z.Y.: An airport passenger terminal simulator: a planning and design tool. Simul. Pract. Theor. 6(4), 387–396 (1998)CrossRefGoogle Scholar
  17. 17.
    Ju, Y., Wang, A., Che, H.: Simulation and optimization for the airport passenger flow. In: International Conference on Wireless Communications, Networking and Mobile Computing (WiCom), pp. 6605–6608 (2007)Google Scholar
  18. 18.
    Kim, B., Lee, G.-G., Yoon, J.-Y., Kim, J.-J., Kim, W.-Y.: A method of counting pedestrians in crowded scenes. In: Huang, D.-S., Wunsch, Donald C., Levine, Daniel S., Jo, K.-H. (eds.) ICIC 2008. LNCS, vol. 5227, pp. 1117–1126. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-85984-0_134 Google Scholar
  19. 19.
    Madas, M.A., Zografos, K.G.: Airport slot allocation: from instruments to strategies. J. Air Trans. Manag. 12(2), 53–62 (2006)CrossRefGoogle Scholar
  20. 20.
    Manataki, I.E., Zografos, K.G.: A generic system dynamics based tool for airport terminal performance analysis. Transp. Res. Part C: Emerg. Technol. 17(4), 428–443 (2009)CrossRefGoogle Scholar
  21. 21.
    Newell, G.F.: Application of Queuing Theory. Chapman and Hall, London (1971)Google Scholar
  22. 22.
    Odoni, A.R., de Neufville, R.: Passenger terminal design. Transp. Res. Part A: Policy Pract. 26(1), 27–35 (1992)Google Scholar
  23. 23.
    Ohori, K., Kobayashi, N., Obata, A., Takahashi, A., Takahashi, S.: Decision support for management of agents’ knowledge and skills with job rotation in service-oriented organization. In: 45th Hawaii International Conference on Systems Science (HICSS-45 2012), Proceedings, Grand Wailea, Maui, HI, USA, 4–7 January, pp. 1492–1501 (2012)Google Scholar
  24. 24.
    Ohori, K., Yamane, S., Kobayashi, N., Obata, A., Takahashi, S.: Agent-based social simulation as an aid to communication between stakeholders. In: Chen, S.-H., Terano, T., Yamamoto, R., Tai, C.-C. (eds.) Advances in Computational Social Science. ASS, vol. 11, pp. 265–277. Springer, Tokyo (2014). doi: 10.1007/978-4-431-54847-8_17 Google Scholar
  25. 25.
    Schultz, M., Fricke, H.: Managing passenger handling at airport terminals individual-based approach for modeling the stochastic passenger behavior. In: Proceedings of the 9th USA/Europe Air Traffic Management Research and Development Seminar, ATM 2011, pp. 438–447 (2011)Google Scholar
  26. 26.
    Takakuwa, S., Oyama, T.: Modeling people flow: simulation analysis of international-departure passenger flows in an airport terminal. Winter Simulation Conference, pp. 1627–1634. Louisiana, New Orleans (2003)Google Scholar
  27. 27.
    Tosic, V.: A review of airport passenger terminal operations analysis and modelling. Transp. Res. Part A: Policy Pract. 26(1), 3–26 (1992)CrossRefGoogle Scholar
  28. 28.
    Tosic, V., Babic, O., Janic, M.: Airport Passenger Terminal simulation, Annals of Operations Research in Air Transportation, Faculty of Transport and Traffic Engineering. University of Belgrade, pp. 83–103 (1983)Google Scholar
  29. 29.
    Wu, P.P.-Y., Mengersen, K.: A review of models and model usage scenarios for an airport complex system. Transp. Res. Part A: Policy Pract. 47, 124–140 (2013)Google Scholar
  30. 30.
    McDermott, T., Rouse, W., Goodman, S., Loper, M.: Multi-level modeling of complex socio-technical systems. Proc. Comput. Sci. 16, 1132–1141 (2013)CrossRefGoogle Scholar
  31. 31.
    Park, H., Clear, T., Rouse, W.B., Basole, R.C., Braunstein, M.L., Brigham, K.L., Cunningham, L.: Multilevel simulations of health delivery systems: a prospective tool for policy, strategy, planning, and management. Serv. Sci. 4(3), 253–268 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hiroaki Yamada
    • 1
  • Kotaro Ohori
    • 1
    • 3
  • Tadashige Iwao
    • 2
    • 3
  • Akifumi Kira
    • 3
    • 4
  • Naoyuki Kamiyama
    • 3
    • 5
  • Hiroaki Yoshida
    • 1
  • Hirokazu Anai
    • 1
    • 3
  1. 1.Fujitsu Laboratories Ltd.KawasakiJapan
  2. 2.Fujitsu Ltd.TokyoJapan
  3. 3.Institute of Mathematics for IndustryKyushu UniversityFukuokaJapan
  4. 4.Faculty of Social and Information StudiesGunma UniversityMaebashiJapan
  5. 5.JST, PRESTOKawaguchiJapan

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