Predicting Airport Runway Configuration
Jacob Avery, Hamsa Balakrishnan

Citation
Jacob Avery, Hamsa Balakrishnan. "Predicting Airport Runway Configuration". Thirteenth USA/Europe Air Traffic Management Research and Development Seminar, 2016.

Abstract
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 airports 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.

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Citation formats

• HTML

  Jacob Avery, Hamsa Balakrishnan. <a
  href="http://www.cps-forces.org/pubs/142.html"
  >Predicting Airport Runway Configuration</a>,
  Thirteenth USA/Europe Air Traffic Management Research and
  Development Seminar, 2016.

• Plain text

  Jacob Avery, Hamsa Balakrishnan. "Predicting Airport
  Runway Configuration". Thirteenth USA/Europe Air
  Traffic Management Research and Development Seminar, 2016.

• BibTeX

  @inproceedings{AveryBalakrishnan16_PredictingAirportRunwayConfiguration,
      author = {Jacob Avery and Hamsa Balakrishnan},
      title = {Predicting Airport Runway Configuration},
      booktitle = {Thirteenth USA/Europe Air Traffic Management
                Research and Development Seminar},
      year = {2016},
      abstract = {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
                airports 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.},
      URL = {http://cps-forces.org/pubs/142.html}
  }
  

Posted by Saurabh Amin on 16 Apr 2016.
Groups: forces
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