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Method for evaluating the landing aircraft sequence under disturbed conditions with the use of Petri nets

  • J. Skorupski (a1) and A. Florowski (a1)
Abstract

One of the important tasks that air traffic management services are faced with today is the task of maximising airport capacity. This can be achieved at the tactical level through proper organisation of air traffic around an airport. In recent years, many methods and algorithms for scheduling aircraft landings have been developed; they take into account various optimisation goals. The aim of this paper was to create a method that would allow one to evaluate landing aircraft sequences resulting from these control algorithms, especially in the presence of random disturbances. This method involves modelling the landing aircraft sequence by using Petri nets. The model and the computer tool that have been developed make it possible to take into account different kinds of disturbances and examine the effectiveness of various control strategies under these conditions. This paper presents two experiments that test disturbances with different characteristics and of different intensities. It has been shown that small but more frequent disturbances lead to the worsening of evaluation scores for a given sequence to a lesser extent than rare but larger disturbances. This is particularly important for control algorithms in which the focus is on high aircraft density. If the type of particular disturbances is properly assessed, then it will be possible to assist the decision-maker (air traffic controller) by providing him/her with quantitative evaluations of possible solutions.

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Corresponding author
jsk@wt.pw.edu.pl
References
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1. Cetek, F.A. and Cetek, C. Simulation modelling of runway capacity for flight training airports, Aeronautical J, 2014, 118, (1200), pp 143154.
2. ICAO. Procedures for Air Navigation Services – Air Traffic Management, Doc. 4444, 2007, International Civil Aviation Organization, Montreal, Canada.
3. PANSA, Operations Manual – ‘Warszawa APP’, Polish Air Navigation Services Agency, 2013, Warsaw.
4. Bennell, J.A., Mesgarpour, M. and Potts, C.N. Airport runway scheduling, 4OR, 2011, 9, (2), pp 115138.
5. Bäuerle, N., Engelhardt-Funke, O. and Kolonko, , , M. On the waiting time of arriving aircrafts and the capacity of airports with one or two runways, European J Operational Research, 2007, 177, pp 11801196.
6. Sölveling, G. and Clarke, J. Scheduling of airport runway operations using stochastic branch and bound methods, Transportation Research Part C: Emerging Technologies, 2014, 45, pp 119137.
7. Lieder, A., Briskorn, D. and Stolletz, R.A. Dynamic programming approach for the aircraft landing problem with aircraft classes, Social Science Research Network, 2013. Available at: http://dx.doi.org/10.2139/ssrn.2391111.
8. Yu, S., Cao, X. and Zhang, J. A real-time schedule method for aircraft landing scheduling problem based on cellular automation, Applied Soft Computing, 2011, 11, pp 34853493.
9. Boyden, N. and Flinger, M. Scheduling aircraft landings to balance workload of ground staff, Computers and Industrial Engineering, 2011, 60, pp 206217.
10. Andreeva-Mori, A., Suzuki, S. and Itoh, E. Rule derivation for arrival aircraft sequencing, Aerospace Science and Technology, 2013, 30, pp 200209.
11. Meare, G.D. and Atkin, J.A.D. Pruning rules for optimal runway sequencing with airline preferences, In: Sheibani, K., Hirsch, P., Nordlander, T.E., Montemanni, R., Sofianopoulou, S. and Faulin, j. (Eds), Proceedings of the 7th International Conference on Applied Operational Research, 15-17 July 2015, Lecture Notes in Management Science, Vol 7, Operational Research Laboratory of Western Canada, Vienna, Austria, pp 7682.
12. Caprì, S. and Ignaccolo, M. Genetic algorithms for solving the aircraft-sequencing problem: the introduction of departures into the dynamic model, J Air Transport Management, 2004, 10, pp 345351.
13. Hansen, J.V. Genetic search methods in air traffic control, Computers and Operations Research, 2004, 31, pp 445459.
14. Balakrishnan, H. and Chandran, B. Algorithms for scheduling runway operations under constrained position shifting, Operations Research, 2010, 58, (6), pp 16501665.
15. Sama, M., D'Adriano, A., D'Adriano, P. and Pacciarelli, D. Optimal aircraft scheduling and routing at a terminal control area during disturbances, Transportation Research Part C: Emerging Technologies, 2014, 47, pp 6185.
16. van Leeuwen, P., Hesselink, H. and Rohling, J. Scheduling aircraft using constraint satisfaction, Electronic Notes in Theoretical Computer Science, 2002, 76, pp 252268.
17. Weigang, L., de Souza, B. B., Crespo, A.M.F. and Alves, D.P. Decision support system in tactical air traffic flow management for air traffic flow controllers, J Air Transport Management, 2008, 14, pp 329336.
18. Matsuno, Y., Tsuchiya, T., Wei, J. and Hwang, I. Stochastic optimal control for aircraft conflict resolution under wind uncertainty, Aerospace Science and Technology, 2015, 43, pp 7788.
19. Tavakkoli-Moghaddam, R., Yaghoubi-Panah, M. and Radmehr, F. Scheduling the sequence of aircraft landings for a single runway using a fuzzy programming approach, J of Air Transport Management, 2012, 25, pp 1518.
20. Boursier, L., Favennec, B., Hoffman, E., Trzmiel, A., Vergne, F. and Zeal, K. Merging arrival flows without heading instructions, 7th USA/Europe Air Traffic Management Research and Development Seminar, 2-5 July 2007, Barcelona, Spain.
21. Berge, M.E., Haraldsdottir, A., Scharl, J. and Zhu, K.H. The generalized arrival planner (GARP) modelling and analysis for arrival planning, 28th International Congress of the Aeronautical Sciences, 2012, Brisbane, Australia.
22. SESAR Annual Report 2012, SESAR Joint Undertaking, 2013, Brussels. Available at: http://www.sesarju.eu/sites/default/files/documents/reports/SESAR_AR2012_web.pdf.
23. Hansen, M. Micro-level analysis of airport delay externalities using deterministic queuing models: a case study, J Air Transport Management, 2002, 8, pp 7387.
24. Stojković, G., Soumis, F., Desrosiers, J. and Solomon, M. M. An optimization model for a real-time flight scheduling problem, Transportation Research Part A: Policy and Practice, 2002, 36, pp 779788.
25. Wu, D. Models, Optimal Performances and Sensitivities of Commercial Flight Trajectory in the Air Traffic System, PhD thesis, 2012, University of Minnesota. Available at: http://hdl.handle.net/11299/144391.
26. Zillies, J.L., Schmitt, A. and Vujasinovic, R. Multiobjective 4D optimization of a trajectory-based air traffic management, Integrated Communications, Navigation and Surveillance Conference (ICNS), 2013, pp 1–11.
27. Asfe, M., Zehi, M., Tash, M. and Yaghoubi, N. Ranking different factors influencing flight delay, Management Science Letters, 2014, 4, (7), pp 13971400.
28. Jensen, K. Coloured Petri Nets. Basic Concepts, Analysis Methods and Practical Use, 1997, Springer Verlag, Berlin.
29. Marsan, M.A., Balbo, G., Conte, G., Donatelli, S. and Franceschinis, G. Modelling with generalized stochastic Petri nets, Torino, Universita degli Studi di Torino, Dipartamento d'Informatica, 1999.
30. Reisig, W. Understanding Petri Nets, Modelling Techniques, Analysis Methods, Case Studies, Springer Verlag, Berlin, 2013.
31. Werther, N., Moehlenbrink, C. and Rudolph, , , M. Colored Petri Net based formal airport control model for simulation and analysis of airport control processes. In: Duffy, V.N. (Ed) Proceedings of the 1st International Conference on Digital Human Modelling (ICDHM'07), 2007, pp 1027–1036.
32. Oberheid, H. and Söffker, D. Cooperative arrival management in air traffic control - A coloured Petri net model of sequence planning. In: Hee, K. and Valk, R. (Eds), Applications and Theory of Petri Nets, 2008, Springer, Berlin, Germany, pp 348367.
33. Skorupski, J. Method of analysis of the relation between serious incident and accident in air traffic. In: Berenguer, C. (Ed), Advances in Safety, Reliability and Risk Management, 2012, CRC Press/Taylor and Francis, London, pp 23932401, (DOI:10.1201/b11433-340).
34. Skorupski, J. The risk of an air accident as a result of a serious incident of the hybrid type, Reliability Engineering and System Safety, 2015, 140, pp 3752.
35. Davidrajuh, R. and Lin, B. Exploring airport traffic capability using Petri net based model. Expert Systems with Applications, 2011, 38, (9), pp 1092310931.
36. Vidosavljević, A. and Toŝić, V. Modelling of turnaround process using Petri nets, Proceedings of the 14th ATRS World Conference, 2010, Porto, Portugal, pp 1–13.
37. Kovacs, A., Nemeth, E. and Hangos, K. Modelling and optimization of runway traffic flow using coloured Petri nets, Proceedings of the 5th International Conference on Control and Automation, 2005, Budapest, Hungary, pp 881–886.
38. Smieszek, H. and Karl, C. An approach to cognitive simulation of air traffic controllers based on coloured Petri nets. In: Soeffker, D. and Kluge, A. (Eds), Kognitive Systeme, 2013, DuEPublico, Duisburg-Essen Publication, Duisburg, Germany. Available at: http://duepublico.uni-duisburg-essen.de/servlets/DerivateServlet/Derivate-33215/104_Smieszek.pdf.
39. Smieszek, H., Manske, P., Hasselberg, A., Russwinkel, N. and Möhlenbrink, C. Cognitive Simulation of Limited Working Memory Capacity Applied to an Air Traffic Control Task. In: West, R. and Stewart, T. (Eds). Proceedings of the 12th International Conference on Cognitive Modelling, 2013, Carleton University, Ottawa, Canada, pp 227–232.
40. Smieszek, H. and Joeres, F. Prospective decision making in a macro-cognitive model of airport traffic control system (MATriCS) based on coloured Petri nets. In: Brandenburg, E., Doria, L., Gross, A., Günzler, T., and Smieszek, H. (Eds), Proceedings of the 10th Berlin Workshop on Human-Machine Systems, Foundations and Applications of Human-Machine Interaction, 2013, Universitätsverlag der Technischen Universität Berlin, pp 505–512.
41. Smieszek, H., Joeres, F. and Russwinkel, N. Workload of airport tower controllers, empirical validation of a macro-cognitive model, In: Jipp, M., Kluge, A., Soeffker, D. and Wendemuth, H. (Eds), Kognitive Systeme 2, 2015, DuEPublico, Duisburg-Essen Publication, Duisburg, Germany. Available at: http://duepublico.uni-duisburg-essen.de/servlets/DerivateServlet/Derivate-38598/ks_vol2_2015_1_Smieszek_Joeres_Russwinkel.pdf.
42. Ratzer, A.V., Wells, L., Lassen, H.M., Laursen, M., Qvortrup, J.F., Stissing, M.S., Westergaard, M., Christensen, S. and Jensen, K. CPN tools for editing, simulating, and analysing coloured Petri nets, Proceedings of the 24th International Conference on Applications and Theory of Petri Nets (Petri Nets 2003), Lecture Notes in Computer Science, 2003, 2679, pp 450–462.
43. Davis, T. J., Erzberger, H., Green, S. M. and Nedell, W. Design and evaluation of an air traffic control final approach spacing tool, J Guidance, Control, and Dynamics, 1991, 14, (4), pp 848854.
44. Guzhva, V.S., Abdelghany, A. and Lipps, T. Experimental approach to NextGen benefits estimation, a case of single-airline aircraft arrival management system, J Air Transport Management, 2014, 35, pp 108116.
45. Skorupski, J. Model of the hierarchical process of managing the approaching air traffic in the terminal area, In: Mikulski, J. (Ed), TST 2015, CCIS 531, 2015, Springer International Publishing, Switzerland, pp 108120.
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