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An Accurate Prediction Method for Airport Operational Situation Based on Hidden Markov Model

  • Xintai Zhang
  • Yanwen Xie
  • Yaping ZhangEmail author
  • Zhiwei Xing
  • Xiao Luo
  • Qian Luo
Conference paper
  • 12 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 617)

Abstract

This paper is mainly devoted to an prediction method for airport operational situation which is one of the most important parts of the airport operation system. In order to provide theoretical support for high-level airport management, field operation management, air traffic control and airlines, and improve the service capacity of the airport, this paper makes a prediction study of the airport operation situation. Hidden Markov (HMM) prediction model is established based on the analysis of airport operation system. Baum-Welch and Viterbi algorithms are used to solve the prediction results. The model is validated and applied in a domestic hub airport. The results show that the prediction accuracy of HMM is 60 and 20% higher than that of Autoregressive Moving Average Model and Grey Markov model, respectively. It can also improve the situation value of airport operation situation, i.e. airport service capability. This method is more suitable for the analysis of airport operation.

Keywords

Airport traffic Operational situation forecast Hierarchical division Hidden markov model 

Notes

Acknowledgements

This research has been supported under the National Natural Science Foundation of China (Grant No. U1533203), Sichuan Science and Technology Project (Grant No. 2019YFG0050), Sichuan Provincial-College Cooperation Science and Technology Project (Grant No. 2019YFSY0024).

References

  1. 1.
    Endsley MR (1995) Toward a theory of situation awareness in dynamic systems. Hum Factors 37(1):32–64CrossRefGoogle Scholar
  2. 2.
    Burns CM, Skraaning G, Jamieson GA et al (2008) Evaluation of ecological interface design for nuclear process control: situation awareness effects. Hum Factors J Hum Factors Ergon Soc 50(4):663–679CrossRefGoogle Scholar
  3. 3.
    Panteli M, Kirschen DS (2015) Situation awareness in power systems: theory, challenges and applications. Electr Power Syst Res 122:140–151CrossRefGoogle Scholar
  4. 4.
    Nonose K, Corver S, Majumdar A et al (2014) A behavioral observation method to assess team situation awareness of air traffic control teams. In: Proceedings of the 26th Australian computer-human interaction conference on designing futures: the future of design. ACM, pp 456–459Google Scholar
  5. 5.
    Blasch E, Wang Z, Shen D et al (2014) Enhanced air operations for ground situational awareness. In: 2014 IEEE/AIAA 33rd digital avionics systems conference (DASC). IEEE, pp 3D2-1–3D2-13Google Scholar
  6. 6.
    Xie L, Wang Y, Yu J (2013) Network security situation awareness based on neural networks. J Tsinghua Univ (Science and Technology) 23(12):1750–1760Google Scholar
  7. 7.
    Xu X, Ren J, Li N (2014) Identification of terminal area traffic situation based on FCM. Aeronaut Comput Tech 44(1):1–4Google Scholar
  8. 8.
    Li N, Ren J, Xu X (2014) Identification of terminal area traffic situation. Sci Technol Eng 14(11):256–261Google Scholar
  9. 9.
    Wei R (2015) Study on the methods of multi-scale prediction for expressway. Jilin UniversityGoogle Scholar
  10. 10.
    Wang G (2016) Researches on flight status estimation and display based on GIS. Civil Aviation University of ChinaGoogle Scholar
  11. 11.
    Kun Qian (2016) Design of traffic operation situation analysis system based on intelligent city intelligent transportation system. China Manage Informationization 19(21):208–210Google Scholar
  12. 12.
    Yuan L (2017) Research on dynamic traffic characteristics and operation situation of terminal area. Nanjing University of Aeronautics and AstronauticsGoogle Scholar
  13. 13.
    Feng C, Jing X, Li Q, Yao P (2017) Theoretical research of decision-making point in air combat based on hidden Markov model. J Beijing Univ Aeronaut Astronaut 43(3):615–626Google Scholar
  14. 14.
    Xu C, Jiang Y, Cai M, Chen L (2018) Joint Scheduling of both taxiway and gate based on bi-level programming. J Beijing Univ Aeronaut AstronautGoogle Scholar
  15. 15.
    Yang W, Du Z, Zhou Y (2017) Overview of airport operation situation awareness system and key technologies. In: 2017 Papers of the world transport congressGoogle Scholar
  16. 16.
    Wang M (2016) Research on association rule discovery and model of warning evaluation about flight cooperative security. Civil Aviation University of ChinaGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Xintai Zhang
    • 1
  • Yanwen Xie
    • 1
  • Yaping Zhang
    • 1
    Email author
  • Zhiwei Xing
    • 2
  • Xiao Luo
    • 3
  • Qian Luo
    • 3
  1. 1.School of Transportation Science and EngineeringHarbin Institute of TechnologyHarbinChina
  2. 2.Research Base of Ground Support Equipment, Civil Aviation University of ChinaTianjinChina
  3. 3.The Second Institute of Civil Aviation Administration of ChinaChengduChina

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