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Tytuł artykułu

Arriving air traffic separations generalized model identification

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Quick development of computer techniques and increasing computational power allow for building high-fidelity models of various complex objects and processes using historical data. One of the processes of this kind is an air traffic, and there is a growing need for traffic mathematical models as air traffic is increasing and becoming more complex to manage. This study concerned the modelling of a part of the arrival process. The first part of the research was air separation modelling by using continuous probability distributions. Fisher information matrix was used for the best fit selection. The second part of the research consisted of applying regression models that best match the parameters of representative distributions. Over a dozen airports were analyzed in the study and that allowed to build a generalized model for aircraft air separation in function of traffic intensity. Results showed that building a generalized model which comprises traffic from various airports is possible. Moreover, aircraft air separation can be expressed by easy to use mathematical functions. Models of this kind can be used for various applications, e.g.: air separation management between aircraft, airports arrival capacity management, and higher-level air traffic simulation or optimization tasks.
Rocznik
Strony
art. no. e140694
Opis fizyczny
Bibliogr. 28 poz., rys., tab.
Twórcy
  • Institute of Aeronautics and Applied Mechanics, Warsaw University of Technology, 00-665 Warsaw, Poland
  • Institute of Aeronautics and Applied Mechanics, Warsaw University of Technology, 00-665 Warsaw, Poland
Bibliografia
  • [1] EUROCONTROL, “Performance Review Report, An Assessment of Air Traffic Management in Europe during the Calendar Year 2019,” Performance Review Commission, EUROCONTROL, 96, rue de la Fusée, B-1130 Brussels, Belgium, Tech. Rep., Jun 2020.
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  • [3] V. Dhar, “Data Science and Prediction,” Comm. ACM, vol. 56, no. 12, pp. 64–73, Dec 2013, doi: 10.1145/2500499.
  • [4] M. Motylewicz andW. Gardziejczyk, “Statistical model for traffic noise prediction in signalised roundabouts,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 4, pp. 937–948, Aug 2020.
  • [5] G. Carrozza, M. Faella, F. Fucci, R. Pietrantuono, and S. Russo, “Engineering air traffic control systems with a model-driven approach,” IEEE Software, vol. 30, no. 3, pp. 42–48, 2013, doi: 10.1109/MS.2013.20.
  • [6] Y. Xu, H. Zhang, Z. Liao, and L. Yang, “A dynamic air traffic model for analyzing relationship patterns of traffic flow parameters in terminal airspace,” Aerosp. Sci. Technol., vol. 55, pp. 10–23, May 2016, doi: 10.1016/j.ast.2016.05.010.
  • [7] L. Yang, S. Yin, and M. Hu, “Network flow dynamics modeling and analysis of arrival traffic in terminal airspace,” IEEE Access, vol. 7, pp. 73 993–74 016, Jun 2019, doi: 10.1109/ACCESS.2019.2921335.
  • [8] D. Delahaye and S. Puechmorel, Modelling and Optimization of Air Traffic. London, UK: Wiley-ISTE, 2013.
  • [9] M. Prandini, L. Piroddi, S. Puechmorel, and S.L. Brázdilová, “Toward air traffic complexity assessment in new generation air traffic management systems,” IEEE Trans. Intell. Transp. Syst., vol. 12, no. 3, pp. 809–818, Sep 2011.
  • [10] P. Gołda, T. Zawisza, and M. Izdebski, “Evaluation of efficiency and reliability of airport processes using simulation tools,” Eksploat. Niezawodn. – Maint. Reliab., vol. 23, no. 4, p. 659–669, 2021, doi: 10.17531/ein.2021.4.8.
  • [11] M. Kowalski, M. Izdebski, J. Żak, P. Gołda, and J. Manerowski, “Planning and management of aircraft maintenance using a genetic algorithm,” Eksploat. Niezawodn. – Maint. Reliab., vol. 23, no. 1, p. 143–153, 2021, doi: 10.17531/ein.2021.1.15.
  • [12] T. Polishchuk, A. Lemetti, and R. Sáez, “Evaluation of Flight Efficiency for Stockholm Arlanda Airport using OpenSky Network Data,” in Proceedings of the 7th OpenSky Workshop 2019, vol. 67, 2019, pp. 13–24.
  • [13] A. Pawełek and P. Lichota, “Arrival air traffic separations assessment using Maximum Likelihood Estimation and Fisher Information Matrix,” in Proceedings of the 20th International Carpathian Control Conference, Kraków-Wieliczka, May 2019, pp. 624–629.
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  • [15] A. Pawelek, P. Lichota, R. Dalmau, and X. Prats, “Fuel-Efficient Trajectories Traffic Synchronization,” J. Aircr., vol. 56, pp. 481–492, Mar 2019, doi: 10.2514/1.C034730.
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  • [17] R. Saez, X. Prats, T. Polishchuk, and V. Polishchuk, “Traffic synchronization in terminal airspace to enable continuous descent operations in trombone sequencing and merging procedures: An implementation study for Frankfurt airport,” Transp. Res. Part C Emerging Technol., vol. 121, no. C121, pp. 1–23, Dec 2020, doi: 10.1016/j.trc.2020.102875.
  • [18] R.V. Jategaonkar, Flight Vehicle System Identification: A Time Domain Methodology, 2nd ed., ser. Progess in Astronautics and Aeronautics. Reston, VA: AIAA, 2015.
  • [19] M. Hazewinkel, “Maximum-likelihood method”, Encyclopedia of Mathematics, 2001st ed. Springer Science+Business Media B.V. / Kluwer Academic Publishers, 1994.
  • [20] P. Lichota, “Aircraft system identification using simultaneous quantized harmonic input signals,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 6, pp. 1351–1362, Dec 2020, doi: 10.24425/bpasts.2020.135397.
  • [21] Y. Pawitan, In All Likelihood: Statistical Modelling And Inference Using Likelihood, 1st ed. The address: Oxford University Press, Mar 2013.
  • [22] N.L. Johnson, S. Kotz, and N. Balakrishnan, Continuous Univariate Distributions, 2nd ed. New York: Wiley, 1994, vol. 1.
  • [23] J.J. Moré, “The Levenberg-Marquardt algorithm: Implementation and theory,” in Numerical Analysis, G.A. Watson, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 1978, pp. 105–116, doi: 10.1007/BFb0067700.
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  • [25] P. Lichota, F. Dul, and A. Karbowski, “System Identification and LQR Controller Design with Incomplete State Observation for Aircraft Trajectory Tracking,” Energies, vol. 13, no. 20, p. 5354, 2020, doi: 10.3390/en13205354.
  • [26] C. Deiler, “Aerodynamic modeling, system identification, and analysis of iced aircraft configurations,” J. Aircr., vol. 55, no. 1, pp. 145–161, 2017, doi: 10.2514/1.C034390.
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  • [28] M. Jacyna, R. Jachimowski, E. Szczepa´nski, and M. Izdebski, “Road vehicle sequencing problem in a railroad intermodal terminal – simulation research,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 5, pp. 1135–1148, Oct 2020, doi: 10.24425/bpasts.2020.134643.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-50f1bc03-c206-41a2-bf5f-d7433749d68e
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