Predicting Airport Runway Configuration: A Discrete-Choice Modeling Approach
The runway configuration is a key driver of airport capacity at any time. Several factors, such as weather conditions (wind and visibility), traffic demand, air traffic controller workload, and the coordination of flows with neighboring airport influence the selection of runway configur...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Federal Aviation Administration/EUROCONTROL
2017
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| Online Access: | http://hdl.handle.net/1721.1/106551 https://orcid.org/0000-0003-1128-0039 https://orcid.org/0000-0002-8624-7041 |
| _version_ | 1811085648515301376 |
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| author | Avery, Jacob Bryan Balakrishnan, Hamsa |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Avery, Jacob Bryan Balakrishnan, Hamsa |
| author_sort | Avery, Jacob Bryan |
| collection | MIT |
| description | The runway configuration is a key driver of airport capacity at any time. Several factors, such as weather conditions (wind and visibility), traffic demand, air traffic controller workload, and the coordination of flows with neighboring airport influence the selection of runway configuration.
This paper identifies a discrete-choice model of the configuration selection process from empirical data. The model reflects the importance of various factors in terms of a utility function. Given the weather, traffic demand and the current runway configuration, the model provides a probabilistic forecast of the runway configuration at the next 15-minute interval. This prediction is then extended to obtain the 3-hour probabilistic
forecast of runway configuration. The proposed approach is illustrated using case studies based on data from LaGuardia (LGA) and San Francisco (SFO) airports, first by assuming
perfect knowledge of weather and demand 3-hours in advance, and then using the Terminal Aerodrome Forecasts (TAFs). The results show that given the actual traffic demand and weather conditions 3 hours in advance, the model predicts the correct
runway configuration at LGA with an accuracy of 82%, and at SFO with an accuracy of 85%. Given the forecast weather and scheduled demand, the accuracy of correct prediction of the runway configuration 3 hours in advance is 80% for LGA and 82% for SFO. |
| first_indexed | 2024-09-23T13:13:02Z |
| format | Article |
| id | mit-1721.1/106551 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:13:02Z |
| publishDate | 2017 |
| publisher | Federal Aviation Administration/EUROCONTROL |
| record_format | dspace |
| spelling | mit-1721.1/1065512022-10-01T13:50:44Z Predicting Airport Runway Configuration: A Discrete-Choice Modeling Approach Avery, Jacob Bryan Balakrishnan, Hamsa Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Avery, Jacob Bryan Balakrishnan, Hamsa The runway configuration is a key driver of airport capacity at any time. Several factors, such as weather conditions (wind and visibility), traffic demand, air traffic controller workload, and the coordination of flows with neighboring airport influence the selection of runway configuration. This paper identifies a discrete-choice model of the configuration selection process from empirical data. The model reflects the importance of various factors in terms of a utility function. Given the weather, traffic demand and the current runway configuration, the model provides a probabilistic forecast of the runway configuration at the next 15-minute interval. This prediction is then extended to obtain the 3-hour probabilistic forecast of runway configuration. The proposed approach is illustrated using case studies based on data from LaGuardia (LGA) and San Francisco (SFO) airports, first by assuming perfect knowledge of weather and demand 3-hours in advance, and then using the Terminal Aerodrome Forecasts (TAFs). The results show that given the actual traffic demand and weather conditions 3 hours in advance, the model predicts the correct runway configuration at LGA with an accuracy of 82%, and at SFO with an accuracy of 85%. Given the forecast weather and scheduled demand, the accuracy of correct prediction of the runway configuration 3 hours in advance is 80% for LGA and 82% for SFO. National Science Foundation (U.S.) (NSF Cyber-Physical Systems project FORCES, grant number 1239054) 2017-01-20T15:45:58Z 2017-01-20T15:45:58Z 2015-06 Article http://purl.org/eprint/type/ConferencePaper 2406-4068 Paper ID 509 http://hdl.handle.net/1721.1/106551 Avery, Jacob and Hamsa Balakrishnan. "Predicting Airport Runway Configuration: A Discrete-Choice Modeling Approach" Eleventh USA/Europe Air Traffic Management Research and Development Seminar, Lisbon, Portugal June 23-26, 2015. https://orcid.org/0000-0003-1128-0039 https://orcid.org/0000-0002-8624-7041 en_US http://www.atmseminarus.org/seminarContent/seminar11/presentations/509-Balakrishnan_0126150652-PresentationPDF-6-29-15.pdf Proceedings of the [Eleventh] USA/Europe Air Traffic Management Research and Development Seminar, ATM2015 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Federal Aviation Administration/EUROCONTROL MIT web domain |
| spellingShingle | Avery, Jacob Bryan Balakrishnan, Hamsa Predicting Airport Runway Configuration: A Discrete-Choice Modeling Approach |
| title | Predicting Airport Runway Configuration: A Discrete-Choice Modeling Approach |
| title_full | Predicting Airport Runway Configuration: A Discrete-Choice Modeling Approach |
| title_fullStr | Predicting Airport Runway Configuration: A Discrete-Choice Modeling Approach |
| title_full_unstemmed | Predicting Airport Runway Configuration: A Discrete-Choice Modeling Approach |
| title_short | Predicting Airport Runway Configuration: A Discrete-Choice Modeling Approach |
| title_sort | predicting airport runway configuration a discrete choice modeling approach |
| url | http://hdl.handle.net/1721.1/106551 https://orcid.org/0000-0003-1128-0039 https://orcid.org/0000-0002-8624-7041 |
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