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Applications of stochastic modeling in air traffic management: Methods, challenges and opportunities for solving air traffic problems under uncertainty

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  • Shone, Rob
  • Glazebrook, Kevin
  • Zografos, Konstantinos G.

Abstract

In this paper we provide a wide-ranging review of the literature on stochastic modeling applications within aviation, with a particular focus on problems involving demand and capacity management and the mitigation of air traffic congestion. From an operations research perspective, the main techniques of interest include analytical queueing theory, stochastic optimal control, robust optimization and stochastic integer programming. Applications of these techniques include the prediction of operational delays at airports, pre-tactical control of aircraft departure times, dynamic control and allocation of scarce airport resources and various others. We provide a critical review of recent developments in the literature and identify promising research opportunities for stochastic modelers within air traffic management.

Suggested Citation

  • Shone, Rob & Glazebrook, Kevin & Zografos, Konstantinos G., 2021. "Applications of stochastic modeling in air traffic management: Methods, challenges and opportunities for solving air traffic problems under uncertainty," European Journal of Operational Research, Elsevier, vol. 292(1), pages 1-26.
    • Handle: RePEc:eee:ejores:v:292:y:2021:i:1:p:1-26
    DOI: 10.1016/j.ejor.2020.10.039
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    Cited by:

    1. Chen, Dan & Yin, Jianan & Zhong, Yugang & Tang, Cheng, 2025. "A two-layer air traffic dynamic network based en route cascading failure propagation dynamics modeling and multi-dimensional impact analysis," Reliability Engineering and System Safety, Elsevier, vol. 264(PA).
    2. Guardo-Martinez, Elisa & Onggo, Stephan & Kunc, Martin & Padrón, Silvia & Tomasella, Maurizio, 2026. "Robust airline scheduling with turnaround under uncertainty: towards collaborative airline scheduling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 205(C).
    3. Li, Mingjie & Hao, Jin-Kao & Wu, Qinghua, 2022. "Learning-driven feasible and infeasible tabu search for airport gate assignment," European Journal of Operational Research, Elsevier, vol. 302(1), pages 172-186.
    4. Sobrie, Léon & Verschelde, Marijn & Roets, Bart, 2024. "Explainable real-time predictive analytics on employee workload in digital railway control rooms," European Journal of Operational Research, Elsevier, vol. 317(2), pages 437-448.
    5. 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).
    6. Liu, Wenjing & Zhao, Qiuhong & Delahaye, Daniel, 2022. "Research on slot allocation for airport network in the presence of uncertainty," Journal of Air Transport Management, Elsevier, vol. 104(C).
    7. Jeffrey Christiansen & Brian Dandurand & Andrew Eberhard & Fabricio Oliveira, 2023. "A study of progressive hedging for stochastic integer programming," Computational Optimization and Applications, Springer, vol. 86(3), pages 989-1034, December.
    8. Du, Sen & Zhong, Gang & Wang, Fei & Wu, Lingxiao & Zhang, Honghai & Xue, Dabin, 2025. "A framework for collaborative UAM traffic flow optimization with mission preferences: Incorporating customized strategy synergy into strategic conflict management," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 202(C).
    9. Carvalho, Lucas Orbolato & Murça, Mayara Condé Rocha, 2025. "A stochastic model-free reinforcement learning framework for optimizing runway capacity management under uncertainty," Transportation Research Part A: Policy and Practice, Elsevier, vol. 200(C).
    10. Bolić, Tatjana & Castelli, Lorenzo & Corolli, Luca & Scaini, Giovanni, 2021. "Flexibility in strategic flight planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    11. Rob Shone & Kevin Glazebrook & Konstantinos G. Zografos, 2024. "A New Simheuristic Approach for Stochastic Runway Scheduling," Transportation Science, INFORMS, vol. 58(2), pages 520-539, March.

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