Predictor of Airport Runway Capacity (PARC)

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: 80NSSC18C0036
Agency Tracking Number: 174171
Amount: $746,076.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: A3
Solicitation Number: SBIR_17_P2
Solicitation Year: 2017
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-04-30
Award End Date (Contract End Date): 2020-04-29
Small Business Information
9971 Valley View Road, Eden Prairie, MN, 55344-3586
DUNS: 052062833
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Sebastian Timar
 (408) 819-9200
Business Contact
 Lisa Knopik
Phone: (952) 829-5864
Research Institution

The Predictor of Airport Runway Capacity (PARC) is a decision support tool for air traffic managers to estimate the near-term capacity of the individual runways of an airport for traffic planning and control. To estimate runway capacity, PARC analyzes historical data describing an airport’s traffic movements, operating conditions, operating procedures, and geospatial data to determine the time intervals between successive aircraft using an airport’s runway and variables that influence it. Variables may include facility, aircraft, flight plan and weather conditions. Second, PARC uses the data to construct Bayesian Network (BN) statistical models of the joint probability of inter-aircraft time spacing and the variables for each airport’s runway. Third, PARC performs Monte-Carlo simulations of the traffic planned to use each runway, sampling the BN models to estimate the spacing of successive takeoff, landing and taxi crossing aircraft, to obtain a distribution of possible runway capacities. Fourth, PARC selects a a target runway capacity from the distribution for airport traffic management. The advantages of PARC are adapting to the characteristics of the airport and accounting for the anticipated operating conditions to provide accurate estimates of runway capacity. Phase I demonstrates processing of FAA System Wide Information Management (SWIM) traceable data sources for modeling for 1-year of data for Atlanta-Hartsfield International Airport (KATL). Phase I uses the data to constructs BN models of inter-aircraft time spacing and to validate the models. Phase I demonstrates the greater accuracy of the BN models in representing and predicting inter-aircraft time spacing than a simpler single-event probability model. Phase I demonstrates sampling of the BN models for different airport, aircraft and flight plan conditions to obtain inter-aircraft spacing values to be used in Monte-Carlo simulations for capacity prediction.

* Information listed above is at the time of submission. *

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