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Subliminal Speed Control in Air Traffic Management: Optimization and Simulation

Author

Listed:
  • David Rey

    (Transport and Traffic Engineering Laboratory (LICIT), IFSTTAR-ENTPE, University of Lyon, 69675 Bron Cedex, France)

  • Christophe Rapine

    (Laboratory G-SCOP, INPG, Université Joseph Fourier, 38301 Grenoble, France)

  • Rémy Fondacci

    (Transport and Traffic Engineering Laboratory (LICIT), IFSTTAR-ENTPE, University of Lyon, 69675 Bron Cedex, France)

  • Nour-Eddin El Faouzi

    (Transport and Traffic Engineering Laboratory (LICIT), IFSTTAR-ENTPE, University of Lyon, 69675 Bron Cedex, France)

Abstract

We address the conflict resolution problem in air traffic management. It is widely acknowledged that air traffic controllers’ (ATCs) workload is related to the density of flights. ATCs’ main task is to ensure the safety of flights throughout their trips and consists of ensuring the respect of separation standards. Recently, the concept of subliminal control has emerged as a promising conflict resolution technique that could be used to reduce the impact of conflicts on ATCs’ workload. In this research, we present deterministic conflict resolution models adapted to subliminal speed control. The proposed models are formulated as nonlinear optimization problems that seek to minimize indicators related to ATCs’ workload (total conflict duration, total number of conflicts) using only minor speed adjustments. We introduce a linear approximation of the aircraft separation equations to implement the obtained mixed integer programs on a continental size air traffic network. Specifically, we develop a simulation framework aiming at reproducing realistic navigation conditions and evaluate the robustness of our conflict resolution models using a generic uncertainty model. We show that the impact of conflicts on ATCs’ workload can be significantly reduced using only limited resources, i.e., a narrow speed modulation range, even in the presence of perturbations. Further, we demonstrate that a significant share of the potential conflicts can be resolved without inducing important delays to flights. Finally, we report that our model does not require extensive computational resources as most instances can be solved to global optimality in a few seconds.

Suggested Citation

  • David Rey & Christophe Rapine & Rémy Fondacci & Nour-Eddin El Faouzi, 2016. "Subliminal Speed Control in Air Traffic Management: Optimization and Simulation," Transportation Science, INFORMS, vol. 50(1), pages 240-262, February.
  • Handle: RePEc:inm:ortrsc:v:50:y:2016:i:1:p:240-262
    DOI: 10.1287/trsc.2015.0602
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    References listed on IDEAS

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    1. Hanif D. Sherali & Raymond W. Staats & Antonio A. Trani, 2003. "An Airspace Planning and Collaborative Decision-Making Model: Part I—Probabilistic Conflicts, Workload, and Equity Considerations," Transportation Science, INFORMS, vol. 37(4), pages 434-456, November.
    2. Hanif D. Sherali & Raymond W. Staats & Antonio A. Trani, 2006. "An Airspace-Planning and Collaborative Decision-Making Model: Part II---Cost Model, Data Considerations, and Computations," Transportation Science, INFORMS, vol. 40(2), pages 147-164, May.
    3. Dimitris Bertsimas & Sarah Stock Patterson, 1998. "The Air Traffic Flow Management Problem with Enroute Capacities," Operations Research, INFORMS, vol. 46(3), pages 406-422, June.
    4. Dimitris Bertsimas & Guglielmo Lulli & Amedeo Odoni, 2011. "An Integer Optimization Approach to Large-Scale Air Traffic Flow Management," Operations Research, INFORMS, vol. 59(1), pages 211-227, February.
    5. Haddad, R. & Carlier, J. & Moukrim, A., 2008. "A new combinatorial approach for coordinating aerial conflicts given uncertainties regarding aircraft speeds," International Journal of Production Economics, Elsevier, vol. 112(1), pages 226-235, March.
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    Citations

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    Cited by:

    1. Cafieri, Sonia & Omheni, Riadh, 2017. "Mixed-integer nonlinear programming for aircraft conflict avoidance by sequentially applying velocity and heading angle changes," European Journal of Operational Research, Elsevier, vol. 260(1), pages 283-290.
    2. Chen, Yunxiang & Zhao, Yifei & Wu, Yexin, 2024. "Recent progress in air traffic flow management: A review," Journal of Air Transport Management, Elsevier, vol. 116(C).
    3. Thibault Lehouillier & Moncef Ilies Nasri & François Soumis & Guy Desaulniers & Jérémy Omer, 2017. "Solving the Air Conflict Resolution Problem Under Uncertainty Using an Iterative Biobjective Mixed Integer Programming Approach," Transportation Science, INFORMS, vol. 51(4), pages 1242-1258, November.
    4. Dias, Fernando H.C. & Hijazi, Hassan & Rey, David, 2022. "Disjunctive linear separation conditions and mixed-integer formulations for aircraft conflict resolution," European Journal of Operational Research, Elsevier, vol. 296(2), pages 520-538.
    5. Mercedes Pelegrín & Martina Cerulli, 2023. "Aircraft Conflict Resolution: A Benchmark Generator," INFORMS Journal on Computing, INFORMS, vol. 35(2), pages 274-285, March.
    6. Sonia Cafieri & Claudia D’Ambrosio, 2018. "Feasibility pump for aircraft deconfliction with speed regulation," Journal of Global Optimization, Springer, vol. 71(3), pages 501-515, July.

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