NASA SBIR 2017 Solicitation

FORM B - PROPOSAL SUMMARY


PROPOSAL NUMBER: 171 A3.02-9118
SUBTOPIC TITLE: Autonomy of the National Airspace Systems (NAS)
PROPOSAL TITLE: Machine Learning of Multi-Modal Influences on Airport Delays

SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
The Innovation Laboratory, Inc.
2360 Southwest Chelmsford Avenue
Portland, OR 97201 - 2265
(503) 242-1761

PRINCIPAL INVESTIGATOR/PROJECT MANAGER (Name, E-mail, Mail Address, City/State/Zip, Phone)
Dr. Jimmy Krozel
Jimmy.Krozel@gmail.com
2360 SW Chelmsford Ave
portland, OR 97201 - 2265
(503) 242-1761

CORPORATE/BUSINESS OFFICIAL (Name, E-mail, Mail Address, City/State/Zip, Phone)
Michelle Camarda
Michelle.Camarda@gmail.com
2360 Southwest Chelmsford Avenue
Portland, OR 97201 - 2265
(503) 242-1761

Estimated Technology Readiness Level (TRL) at beginning and end of contract:
Begin: 2
End: 3

Technology Available (TAV) Subtopics
Autonomy of the National Airspace Systems (NAS) is a Technology Available (TAV) subtopic that includes NASA Intellectual Property (IP). Do you plan to use the NASA IP under the award?
No

TECHNICAL ABSTRACT (Limit 2000 characters, approximately 200 words)
We build machine learning capabilities that enables improved prediction of off-block times and wheels up times which are critical inputs to NAS stakeholders. NextGen will rely on machine learning techniques utilizing all sources of useful information in order to improve predictive accuracy and reliability of flight operations in the NAS. These predictive capabilities will support real-time optimization of surface operations. We use machine learning to learn from historical data and similar situations in the past in order to optimize the performance of the NAS for the current situation. The proposed Multi-Level, Multi-View (MLMV) machine learning approach takes real-time weather, demand, and other data inputs (including landside data from TSA security line queues and traffic congestion levels on highways), searches through an archived set of historical data, identifies similar situations and NAS controls used in those situations, ranks historical situations according to their effectiveness, estimates a set of Traffic Management Initiatives (TMIs) and other control strategies impacting off-block-times and wheels up times.

POTENTIAL NASA COMMERCIAL APPLICATIONS (Limit 1500 characters, approximately 150 words)
NASA's Airspace Operations and Safety Program (AOSP) Shadow Mode Assessment Using Realistic Technologies for the National Airspace System (SMART-NAS) Project will benefit from machine learning. Additionally, NASA can use this technology in: Integrated Arrival/Departure/Surface Operations (IADS), Weather Integrated Decision Making (Wx Integration), Spot and Runway Departure Advisor (SARDA), and Precision Departure Release Capability (PDRC).

POTENTIAL NON-NASA COMMERCIAL APPLICATIONS (Limit 1500 characters, approximately 150 words)
The FAA Air Traffic Service Provider (ATSP) can use our SBIR technology for better control of airports in the NAS. A commercial product can be customized and implemented under contract to Airline Operations Centers (AOCs) for use by dispatchers and ATC coordinators. In such applications, when the ATSP is deciding on taking a certain TMI action, for instance, as discussed in a Collaborative Decision Making (CDM) telecom that occurs on a 2-hour basis, the AOC user can run forward in time through the remainder of the schedule for the day to see if delays will propagate, if weather impacts will cause cancellations or delays, or if crew curfew limits will be negatively affected. AOCs could use SMART NAS to make decisions about what is the best course of action in response to TMIs that could soon be implemented.

TECHNOLOGY TAXONOMY MAPPING (NASA's technology taxonomy has been developed by the SBIR-STTR program to disseminate awareness of proposed and awarded R/R&D in the agency. It is a listing of over 100 technologies, sorted into broad categories, of interest to NASA.)
Air Transportation & Safety
Algorithms/Control Software & Systems (see also Autonomous Systems)
Condition Monitoring (see also Sensors)
Data Acquisition (see also Sensors)
Data Processing
Knowledge Management
Process Monitoring & Control

Form Generated on 04-19-17 12:59