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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Endsley MR (1995) Toward a theory of situation awareness in dynamic systems. Hum Factors 37(1):32–64
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):663–679
Panteli M, Kirschen DS (2015) Situation awareness in power systems: theory, challenges and applications. Electr Power Syst Res 122:140–151
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 456–459
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 3D2-1–3D2-13
Xie L, Wang Y, Yu J (2013) Network security situation awareness based on neural networks. J Tsinghua Univ (Science and Technology) 23(12):1750–1760
Xu X, Ren J, Li N (2014) Identification of terminal area traffic situation based on FCM. Aeronaut Comput Tech 44(1):1–4
Li N, Ren J, Xu X (2014) Identification of terminal area traffic situation. Sci Technol Eng 14(11):256–261
Wei R (2015) Study on the methods of multi-scale prediction for expressway. Jilin University
Wang G (2016) Researches on flight status estimation and display based on GIS. Civil Aviation University of China
Kun Qian (2016) Design of traffic operation situation analysis system based on intelligent city intelligent transportation system. China Manage Informationization 19(21):208–210
Yuan L (2017) Research on dynamic traffic characteristics and operation situation of terminal area. Nanjing University of Aeronautics and Astronautics
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):615–626
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 Astronaut
Yang W, Du Z, Zhou Y (2017) Overview of airport operation situation awareness system and key technologies. In: 2017 Papers of the world transport congress
Wang M (2016) Research on association rule discovery and model of warning evaluation about flight cooperative security. Civil Aviation University of China
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, X., Xie, Y., Zhang, Y., Xing, Z., Luo, X., Luo, Q. (2020). An Accurate Prediction Method for Airport Operational Situation Based on Hidden Markov Model. In: Wang, W., Baumann, M., Jiang, X. (eds) Green, Smart and Connected Transportation Systems. Lecture Notes in Electrical Engineering, vol 617. Springer, Singapore. https://doi.org/10.1007/978-981-15-0644-4_75
Download citation
DOI: https://doi.org/10.1007/978-981-15-0644-4_75
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0643-7
Online ISBN: 978-981-15-0644-4
eBook Packages: EngineeringEngineering (R0)