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Identification of Robust Terminal-Area Routes in Convective Weather

Author

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  • Diana Michalek Pfeil

    (Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Hamsa Balakrishnan

    (Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

Abstract

Convective weather is responsible for large delays and widespread disruptions in the U.S. National Airspace System, especially during summer. Traffic flow management algorithms require reliable forecasts of route blockage to schedule and route traffic. This paper demonstrates how raw convective weather forecasts, which provide deterministic predictions of the vertically integrated liquid (the precipitation content in a column of airspace) can be translated into probabilistic forecasts of whether or not a terminal area route will be blocked. Given a flight route through the terminal area, we apply techniques from machine learning to determine the likelihood that the route will be open in actual weather. The likelihood is then used to optimize terminal-area operations by dynamically moving arrival and departure routes to maximize the expected capacity of the terminal area. Experiments using real weather scenarios on stormy days show that our algorithms recommend that a terminal-area route be modified 30% of the time, opening up 13% more available routes that were forecast to be blocked during these scenarios. The error rate is low, with only 5% of cases corresponding to a modified route being blocked in reality, whereas the original route is in fact open. In addition, for routes predicted to be open with probability 0.95 or greater by our method, 96% of these routes (on average over time horizon) are indeed open in the weather that materializes.

Suggested Citation

  • Diana Michalek Pfeil & Hamsa Balakrishnan, 2012. "Identification of Robust Terminal-Area Routes in Convective Weather," Transportation Science, INFORMS, vol. 46(1), pages 56-73, February.
  • Handle: RePEc:inm:ortrsc:v:46:y:2012:i:1:p:56-73
    DOI: 10.1287/trsc.1110.0372
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    References listed on IDEAS

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    1. 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.
    2. Dimitris Bertsimas & Sarah Stock Patterson, 2000. "The Traffic Flow Management Rerouting Problem in Air Traffic Control: A Dynamic Network Flow Approach," Transportation Science, INFORMS, vol. 34(3), pages 239-255, August.
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    Cited by:

    1. Hongyong Fu & Bin Dan & Xiangkai Sun, 2014. "Joint Optimal Pricing and Ordering Decisions for Seasonal Products with Weather-Sensitive Demand," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-8, March.
    2. Yu, Bin & Guo, Zhen & Asian, Sobhan & Wang, Huaizhu & Chen, Gang, 2019. "Flight delay prediction for commercial air transport: A deep learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 203-221.
    3. Yong Tian & Bojia Ye & Lili Wan & Minhao Yang & Dawei Xing, 2019. "Restricted Airspace Unit Identification Using Density-Based Spatial Clustering of Applications with Noise," Sustainability, MDPI, vol. 11(21), pages 1-15, October.
    4. Chen, Zhenhua & Wang, Yuxuan & Zhou, Lei, 2021. "Predicting weather-induced delays of high-speed rail and aviation in China," Transport Policy, Elsevier, vol. 101(C), pages 1-13.

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