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Trajectory deconfliction with constraint programming

Published online by Cambridge University Press:  26 July 2012

Nicolas Barnier  and
Cyril Allignol
Nicolas Barnier*
Affiliation:
École Nationale de l'Aviation Civile, 7 Av. Édouard Belin, 31055 Toulouse, France; e-mail: barnier@recherche.enac.fr
Cyril Allignol*
Affiliation:
Direction des Services de la Navigation Aèrienne/Direction de la Technique et de l’ Innovation, 7 Av. Édouard Belin, 31055 Toulouse, France; e-mail: allignol@tls.cena.fr
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Abstract

As acknowledged by the SESAR (Single European Sky ATM (Air Traffic Management) Research) program, current Air Traffic Control (ATC) systems must be drastically improved to accommodate the predicted traffic growth in Europe. In this context, the Episode 3 project aims at assessing the performance of new ATM concepts, like 4D-trajectory planning and strategic deconfliction.

One of the bottlenecks impeding ATC performances is the hourly capacity constraints defined on each en-route ATC sector to limit the rate of aircraft. Previous works were mainly focused on optimizing the current ground holding slot allocation process devised to satisfy these constraints. We propose to estimate the cost of directly solving all conflicts in the upper airspace with ground holding, provided that aircraft were able to follow their trajectories accurately.

We present a Constraint Programming model of this large-scale combinatorial optimization problem and the results obtained with the FaCiLe (Functional Constraint Library). We study the effect of uncertainties on the departure time and estimate the cost of improving the robustness of our solutions with the Complete Air Traffic Simulator (CATS). Encouraging results were obtained without uncertainty but the costs of robust solutions are prohibitive. Our approach may however be improved, for example, with a prior flight level allocation and the dynamic resolution of remaining conflicts with one of CATS’ modules.

Type
Articles
Information
The Knowledge Engineering Review , Volume 27 , Special Issue 3: Constraint Programming for Air Traffic Management , 26 July 2012 , pp. 291 - 307
Copyright
Copyright © Cambridge University Press 2012

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References

ACARE 2004. Strategic Research Agenda 2 (SRA 2). Technical report, Advisory Council for Aeronautics Research in Europe.Google Scholar
Alliot, J.-M., Bosc, J.-F., Durand, N., Maugis, L. 1997. CATS: a complete air traffic simulator. In Proceedings of the 16th Digital Avionics Systems Conference DASC'97. Irvine (CA), USA.Google Scholar
Alliot, J.-M., Colin de Verdière, D. 2003. ATM: 20 ans d'effort et perspectives. In Symposium de l'Académie Nationale de l'Air et de l'Espace : vers l'automatisation du vol et sa gestion. Ecole Nationale Supérieure de l'Aéronautique et de l'Espace, Toulouse, France.Google Scholar
Archambault, N. 2004. Speed uncertainty and speed regulation in conflict detection and resolution in air traffic control. In Proceedings of the International Conference on Research in Air Transportation ICRAT'2004. Zilina, Slovakia.Google Scholar
Baptiste, P., Le Pape, C., Nuijten, W. 2001. Constraint-Based Scheduling, Applying Constraint Programming to Scheduling Problems. Kluwer's International Series in Operations Research & Management Science, Springer.CrossRefGoogle Scholar
Barnier, N. 2002. Application de la programmation par contraintes à des problèmes de gestion du trafic aérien. PhD thesis, Institut National Polytechnique de Toulouse. Toulouse, France.Google Scholar
Barnier, N., Allignol, C. 2009. 4D-trajectory deconfliction through departure time adjustment. In Proceedings of the 8th USA/Europe R&D Seminar on Air Traffic Management ATM'2009. Napa, CA, USA.Google Scholar
Barnier, N., Brisset, P. 2001. FaCiLe: a Functional Constraint Library. In Proceedings of the Colloquium on Implementation of Constraint and LOgic Programming Systems CICLOPS'01 (Workshop of CP'01). Paphos, Cyprus.Google Scholar
Barnier, N., Brisset, P. 2002. Graph coloring for air traffic flow management. In Proceedings of the CPAIOR'02: 4th International Workshop on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimisation Problems. Le Croisic, France, 133–147.Google Scholar
Barnier, N., Brisset, P., Rivière, T. 2001. Slot allocation with constraint programming: models and results. In Proceedings of the 4th USA/Europe R&D Seminar on Air Traffic Management ATM'2001. Santa Fe (NM), USA.Google Scholar
Central Office for Delay Analysis 2009. CODA Digest – Delays to Air Transport in Europe. Technical report, Eurocontrol.Google Scholar
Central Flow Management Unit (CFMU) 2000. Basic CFMU Handbook – General & CFMU Systems, 6.0 edn. Eurocontrol (CFMU), Brussels.Google Scholar
Cook, A. J., Tanner, G., Anderson, S. 2004. Evaluating the true cost to airlines of one minute of airborne or ground delay: final report. Technical report, Eurocontrol.Google Scholar
Dalichampt, M., Petit, E., Junker, U., Lebreton, J. 1997. Innovative slot allocation (ISA). Technical report, Eurocontrol.Google Scholar
de Berg, M., van Kreveld, M., Overmars, M., Schwarzkopf, O. 1998. Computational Geometry – Algorithms and Applications. Springer.Google Scholar
Flener, P., Pearson, J., Ågren, M., Garcia Avello, C., Çelitkin, M., Dissing, S. 2007. Air-traffic complexity resolution in multi-sector planning. Journal of Air Transport Management 13(6), 323–328.CrossRefGoogle Scholar
Garot, J.-M., Durand, N. 2005. Failures in the automation of air traffic control. In Proceedings of the Colloque de l'Association Aéronautique et Astronautique de France 3AF. Toulouse, France.Google Scholar
Gianazza, D., Guittet, K. 2007. Selection and evaluation of air traffic complexity metrics. In Proceedings of the 25th Digital Avionics Systems Conference DASC'07. Portland (OR), USA.CrossRefGoogle Scholar
Graham, R., Young, D. 2006. Preparing an Initial Assessment of the SESAR Concept of Operations “EP3: Single European Sky Implementation Support through Validation”. Technical report, Eurocontrol Experimental Centre, Brétigny, France.Google Scholar
Granger, G. 2002. Détection et résolution de conflits aériens : modélisations et analyse. PhD thesis, École Polytechnique. Palaiseau, France.Google Scholar
Granger, G., Durand, N., Alliot, J.-M. 2001. Optimal resolution of en route conflicts. In Proceedings of the 4th USA/Europe R&D Seminar on Air Traffic Management ATM'01. Santa Fe (NM), USA.Google Scholar
Tran Dac, H., Baptiste, P. 2003. Airspace sectorization by constraint programming. In Proceedings of the Rencontres en Informatique Vietnam-France RIVF'03. Hanoi, Vietnam.Google Scholar
Van Hentenryck, P., Simonis, H., Dincbas, M. 1992. Constraint satisfaction using constraint logic programming. Artificial Intelligence 58(1–3), 113–159.CrossRefGoogle Scholar