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Conflict Detection and Resolution with Local Search Algorithms for 4D-Navigation in ATM

  • Vitor Filincowsky Ribeiro
  • Henrique Torres de Almeida Rodrigues
  • Vitor Bona de Faria
  • Weigang LiEmail author
  • Reinaldo Crispiniano Garcia
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)

Abstract

Implementation of Trajectory Based Operations (TBO) has been updating the structure of the advanced Air Traffic Management (ATM). Although several methodologies for conflict detection and resolution (CDR) have been developed to the aviation community, the legacy problem is to find an efficient scheme to present the trajectories in this complex network with massive data and further to detect and resolve the conflicts. In this research we develop a CDR framework based on the management of predicted 4D-trajectories using a Not Only SQL (NoSQL) database and local search algorithms for conflict resolution. This paper describes the architecture and algorithms of the proposed solution in 4-Dimensional Trajectory (4DT). With the application of Trajectory Prediction (TP) simulator using the Brazilian flight plan database, the results from case study show the effectiveness of the proposed methods for this sophisticated problem in ATM.

Keywords

4D trajectory Air Traffic Management Local search algorithms Trajectory-based operations 

Notes

Acknowledgements

This work has been partially supported by the Brazilian National Council for Scientific and Technological Development (CNPq) under the grant number 311441/2017-3 and also by Boeing Research & Technology/Brazil.

References

  1. 1.
    IATA: 2036 Forecast reveals air passengers will nearly double to 7.8 billion. 2017, press Release No.: 55, 24 October 2017Google Scholar
  2. 2.
    Radiöic, T.: The effect of trajectory-based operations on air traffic complexity. Ph.D. dissertation, University of Zagreb, Faculty of Transport and Traffic Sciences (2014)Google Scholar
  3. 3.
    JPDO: Concept of Operations for the Next Generation Air Transportation System. Joint Planning and Development Office, no. Version 3.2 (2011)Google Scholar
  4. 4.
    SESAR: Mission Trajectory Detailed Concept. Single European Sky ATM Research, Technical report, Document identifier DDS/CM/SPM/SESAR/12-042 (2012)Google Scholar
  5. 5.
    López-Leonés, J., Vilaplana, M., Gallo, E., Navarro, F., Querejeta, C.: The aircraft intent description language: a key enabler for air-ground synchronization in trajectory-based operations. In: IEEE/AIAA 26th Digital Avionics Systems Conference, Dallas (2007)Google Scholar
  6. 6.
    Frontera, G., Besada, J., Bernardos, A., Casado, E., López-Leonés, J.: Formal intent-based trajectory description languages. IEEE Trans. Intell. Transp. Syst. 15(4), 1550–1566 (2014)CrossRefGoogle Scholar
  7. 7.
    DECEA: Implementação Operacional do Conceito de Navegação Baseada em Performance (PBN) no Espaço Aéreo Brasileiro. Airspace Control Department, Aeronautical Information Report 24/13 (2013)Google Scholar
  8. 8.
    Dieudonne, J., Crane, H., Jones, S., Smith, C., Remillard, S., Snead, G.: NEO (NextGen 4D TM) Provided by SWIM’s Surveillance SOA (SDN ASP for RNP 4D Ops). In: Integrated Communications, Navigation e Surveillance Conference (ICNS), Herndon (2007)Google Scholar
  9. 9.
    Wandelt, S., Sun, X.: Efficient compression of 4D-trajectory data in air traffic management. IEEE Trans. Intell. Transp. Syst. 16, 844–853 (2015)Google Scholar
  10. 10.
    Song, Y., Cheng, P., Mu, C.: An improved trajectory prediction algorithm based on trajectory data mining for air traffic management. In: IEEE International Conference on Information and Automation, Shenyang, pp. 981–986 (2012)Google Scholar
  11. 11.
    Bongiorno, C., Micciché, S., Mantegna, R.N.: An empirically grounded agent based model for modeling directs, conflict detection and resolution operations in air traffic management. PLoS ONE 12(4), e0175036 (2017)CrossRefGoogle Scholar
  12. 12.
    Mateos, A., Jiménez-Martín, A.: Multiobjective simulated annealing for collision avoidance in ATM accounting for three admissible maneuvers. Math. Prob. Eng. 2016, Article ID 8738014 (2016)Google Scholar
  13. 13.
    Durand, N., Allignol, C., Barnier, N.: A ground holding model for aircraft deconfliction. In: 29th Digital Avionics Systems Conference, Salt Lake City (2010)Google Scholar
  14. 14.
    Bertsimas, D., Lulli, G., Odoni, A.: An integer optimization approach to large-scale air traffic flow management. Oper. Res. 59(1), 211–227 (2011)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Ruiz, S., Piera, M., Nosedal, J., Ranieri, A.: Strategic de-confliction in the presence of a large number of 4D trajectories using a causal modeling approach. Transp. Res. Part C: Emerg. Technol. 39, 39:129–39:147 (2014)CrossRefGoogle Scholar
  16. 16.
    Berling, J., Lau, A., Gollnick, V.: European air traffic flow management with strategic deconfliction. In: Operations Research Proceedings, pp. 279–286 (2017)Google Scholar
  17. 17.
    Dowsland, K.A., Thompson, J.M.: Simulated annealing, pp. 1623–1655. Springer, Heidelberg (2012)Google Scholar
  18. 18.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall, Upper Saddle River (2010)zbMATHGoogle Scholar
  19. 19.
    Besada, J., Frontera, G., Crespo, J., Casado, E., López-Leonés, J.: Automated aircraft trajectory prediction based on formal intent-related language processing. IEEE Trans. Intell. Transp. Syst. 14(3), 1067–1082 (2013)CrossRefGoogle Scholar
  20. 20.
    Ribeiro, V.F., Pamplona, D.A., Fregnani, J.A.T.G., de Oliveira, I.R., Weigang, L.: Modeling the swarm optimization to build effective continuous descent arrival sequences. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, pp. 760–765, November 2016Google Scholar
  21. 21.
    Ferreira, G.R., Felipe Junior, C., de Oliveira, D.: Uso de SGDBs NoSQL na Gerência da Proveniência Distribuída em Workflows Científicos. In: 29th Brazilian Symposium on Databases (SBBD). Brazilian Computing Society, Curitiba (2014)Google Scholar
  22. 22.
    Rodrigues, H.T.A., de Faria, V.B.: Detecção e Resolução de Conflitos para Gerenciamento de Tráfego Aéreo em Trajetórias 4D. Bachelor’s thesis, TransLab, University of Brasilia (2018)Google Scholar
  23. 23.
    Roskam, J.: Airplane Design Part VIII: Airplane Cost Estimation: Design, Development, Manufacturing and Operating, 1st edn. Roskam Aviation and Engineering Corporation (1990)Google Scholar
  24. 24.
    DECEA: Anuário Estatístico de Tráfego Aéreo. Headquarters of Air Navigation Management (CGNA), Technical report (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Vitor Filincowsky Ribeiro
    • 1
  • Henrique Torres de Almeida Rodrigues
    • 1
  • Vitor Bona de Faria
    • 1
  • Weigang Li
    • 1
    Email author
  • Reinaldo Crispiniano Garcia
    • 1
  1. 1.Universidade de Brasília (UnB)BrasiliaBrazil

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