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A dynamic rerouting model for air traffic flow management

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  • Mukherjee, Avijit
  • Hansen, Mark

Abstract

In this paper, we present a stochastic integer programming model for managing air traffic inbound to an airport when both the airport itself and its approach routes are subject to adverse weather. In the model, ground delay decisions are static, while those on rerouting are dynamic. The decision variables in the model are aggregate number of flights planned to arrive at various capacity constrained resources. The model does not directly assign arrival times to individual flights. Therefore, in context of Collaborative Decision Making, which is the governing philosophy of the air traffic management system of the United States, the solutions from the dynamic rerouting model can be directly fed to some resource allocation algorithm that assigns routes and release times to individual flights or to the airlines who operate them. When adverse weather blocks or severely limits capacity of terminal approach routes, rerouting flights onto other approaches yields substantial benefits by alleviating high ground delays. Our experimental results indicate that making rerouting decisions dynamically results in 10-15% delay cost reduction compared to static rerouting, and about 50% less delay cost compared to a "pure" ground holding strategy (i.e., no rerouting). In contrast to static rerouting, the dynamic rerouting capability results in making rerouting decisions that are better matched to realized weather conditions. Lower total expected delay cost is achieved by delaying the rerouting decisions for flights until they reach the divergence point between alternative routes, and hence exploiting updated information on weather while making those decisions. In cases where the airport is the main, but not the only, bottleneck, the dynamic rerouting model may assign higher ground delays so that the rerouting decisions can be deferred until more information on en route weather becomes available.

Suggested Citation

  • Mukherjee, Avijit & Hansen, Mark, 2009. "A dynamic rerouting model for air traffic flow management," Transportation Research Part B: Methodological, Elsevier, vol. 43(1), pages 159-171, January.
  • Handle: RePEc:eee:transb:v:43:y:2009:i:1:p:159-171
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    References listed on IDEAS

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    1. Mukherjee, Avijit, 2004. "Dynamic Stochastic Optimization Models for Air Traffic Flow Management," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt2vk8w6nc, Institute of Transportation Studies, UC Berkeley.
    2. Liu, Pei-chen Barry & Hansen, Mark & Mukherjee, Avijit, 2008. "Scenario-based air traffic flow management: From theory to practice," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 685-702, August.
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    3. Murça, Mayara Condé Rocha, 2018. "Collaborative air traffic flow management: Incorporating airline preferences in rerouting decisions," Journal of Air Transport Management, Elsevier, vol. 71(C), pages 97-107.
    4. Zhang, Qiuhan & Le, Meilong & Xu, Yan, 2021. "Collaborative delay management towards demand-capacity balancing within User Driven Prioritisation Process," Journal of Air Transport Management, Elsevier, vol. 91(C).
    5. Yong Tian & Bojia Ye & Marc Sáez Estupiñá & Lili Wan, 2018. "Stochastic Simulation Optimization for Route Selection Strategy Based on Flight Delay Cost," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 35(06), pages 1-24, December.
    6. Kim, Amy & Hansen, Mark, 2013. "Deconstructing delay: A non-parametric approach to analyzing delay changes in single server queuing systems," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 119-133.
    7. Chen, Dan & Hu, Minghua & Zhang, Honghai & Yin, Jianan & Han, Ke, 2017. "A network based dynamic air traffic flow model for en route airspace system traffic flow optimization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 106(C), pages 1-19.
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    9. Xu, Yan & Dalmau, Ramon & Melgosa, Marc & Montlaur, Adeline & Prats, Xavier, 2020. "A framework for collaborative air traffic flow management minimizing costs for airspace users: Enabling trajectory options and flexible pre-tactical delay management," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 229-255.
    10. Chen, J. & Chen, L. & Sun, D., 2017. "Air traffic flow management under uncertainty using chance-constrained optimization," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 124-141.
    11. John-Paul B. Clarke & Senay Solak & Liling Ren & Adan E. Vela, 2013. "Determining Stochastic Airspace Capacity for Air Traffic Flow Management," Transportation Science, INFORMS, vol. 47(4), pages 542-559, November.
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