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Machine Learning of Multi-Modal Influences on Airport Delays

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: 80NSSC18C0167
Agency Tracking Number: 174882
Amount: $750,000.00
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): 2018-05-18
Award End Date (Contract End Date): 2020-05-17
Small Business Information
2360 SW Chelmsford Avenue
Portland, OR 97201-2265
United States
DUNS: 802036496
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Jimmy Krozel
 (503) 242-1761
Business Contact
 Michelle Camarda
Phone: (503) 242-1761
Research Institution

This SBIR system is a machine learning system that uses a very large database of airside and landside data to predict pushback and takeoff times of aircraft at a given airport.  Airside data sources describe the state of the system after TSA security screening is complete, and includes information about the crew and passengers arriving at the departure gate, turnaround and pushback preparation, ramp and taxiway movement, and aircraft arrival to and departure from the gates.  Landside data sources describe the state of the airport prior to TSA screening, including TSA queue line delays, passenger movement through the airport via cameras, parking availability, road transit delays, congestion, and accidents, and weather conditions. These data are used to classify the current day data using cluster analysis, and take off time and pushback time predictions are made based on the cluster analysis results.

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

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