Identification of Robust Terminal-Area Routes in Convective Weather
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 weat...
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| Format: | Article |
| Language: | en_US |
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Institute for Operations Research and the Management Sciences (INFORMS)
2013
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| Online Access: | http://hdl.handle.net/1721.1/80878 https://orcid.org/0000-0002-8624-7041 |
| _version_ | 1826216137509044224 |
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| author | Balakrishnan, Hamsa Pfeil, Diana Michalek |
| author2 | Massachusetts Institute of Technology. Engineering Systems Division |
| author_facet | Massachusetts Institute of Technology. Engineering Systems Division Balakrishnan, Hamsa Pfeil, Diana Michalek |
| author_sort | Balakrishnan, Hamsa |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T16:42:52Z |
| format | Article |
| id | mit-1721.1/80878 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:42:52Z |
| publishDate | 2013 |
| publisher | Institute for Operations Research and the Management Sciences (INFORMS) |
| record_format | dspace |
| spelling | mit-1721.1/808782022-10-03T07:44:55Z Identification of Robust Terminal-Area Routes in Convective Weather Balakrishnan, Hamsa Pfeil, Diana Michalek Massachusetts Institute of Technology. Engineering Systems Division Balakrishnan, Hamsa Pfeil, Diana Michalek 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. United States. National Aeronautics and Space Administration (NGATS-ATM Airspace Program NNA06CN24A) National Science Foundation (U.S.) (ECCS-0745237) 2013-09-23T18:16:47Z 2013-09-23T18:16:47Z 2011-06 2011-01 Article http://purl.org/eprint/type/JournalArticle 0041-1655 1526-5447 http://hdl.handle.net/1721.1/80878 Pfeil, D. M., and H. Balakrishnan. “Identification of Robust Terminal-Area Routes in Convective Weather.” Transportation Science 46, no. 1 (February 17, 2012): 56-73. https://orcid.org/0000-0002-8624-7041 en_US http://dx.doi.org/10.1287/trsc.1110.0372 Transportation Science Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Institute for Operations Research and the Management Sciences (INFORMS) MIT web domain |
| spellingShingle | Balakrishnan, Hamsa Pfeil, Diana Michalek Identification of Robust Terminal-Area Routes in Convective Weather |
| title | Identification of Robust Terminal-Area Routes in Convective Weather |
| title_full | Identification of Robust Terminal-Area Routes in Convective Weather |
| title_fullStr | Identification of Robust Terminal-Area Routes in Convective Weather |
| title_full_unstemmed | Identification of Robust Terminal-Area Routes in Convective Weather |
| title_short | Identification of Robust Terminal-Area Routes in Convective Weather |
| title_sort | identification of robust terminal area routes in convective weather |
| url | http://hdl.handle.net/1721.1/80878 https://orcid.org/0000-0002-8624-7041 |
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