Small Business Innovation Research/Small Business Tech Transfer
Increasingly Autonomous Traffic Flow Management Under Uncertainty
Project Description
Today, traffic managers largely rely on their intuition for making Traffic Management Initiative (TMI) decisions due to lack of decision aids. As a result TMIs are often inefficient and there is a lot of variability in their application across similar situations. NASA's 'Similar Days in the National Airspace System (NAS)' research addresses this issue, but, the research tools produce not a single recommended TMI choice but an array of choices, with the final decision again left to the manager's intuition. The proposed SBIR research provides a capability for down-selecting to the most effective TMI choice by developing a what-if analysis functionality for exploring multiple TMI options by realistically simulating NAS-wide operations under the influence of individual TMI options. This what-if analysis capability achieves accurate modeling of NAS traffic flows under uncertainty by creatively integrating two innovations. The first is a traffic flow modeling framework for enabling fast and accurate simulation of individual aircraft transits through the NAS network. This traffic flow modeling framework, which we call the Hybrid Traffic Flow model combines desirable features of trajectory-based models with aggregate traffic flow models to allow fast, near real-time NAS performance evaluation under multiple candidate TMI options. Each option is evaluated under multiple scenarios to capture the whole range of possibilities as per the underlying real world uncertainties,. The second is Bayesian Networks for modeling variations caused by underlying NAS uncertainty factors with explicit encoding of human reasoning behind multiple influencing decisions (e.g., Center MIT restriction impositions, airline cancellations), this enables realistic traffic demand and capacity forecasting for feeding the traffic flow model-based TFM evaluations.
More »
Anticipated Benefits
The proposed technology provides NASA with a what-if analysis TFM tool for use in its SASO research program (especially the Autonomous TFM sub-project) for the purpose of evaluating different TFM algorithms of varying levels of autonomy. The proposed what-if analysis capability can also be used by NASA's Airspace Technology Demonstration-3 (ATD-3) researchers as a testing platform for candidate rerouting strategies recommended by rerouting technologies such as MFCR, ORC, and DRAW being developed at NASA. Our what-if analysis capability provides a credible V&V platform for proving the operational feasibility and benefits of the reroutes recommended by NASA's DSTs. The proposed BN-based uncertainty models can be integrated into NASA's SMART-NAS Test Bed to provide a much needed capability to simulate propagation of delays across the NAS network along with the involved human controller actions in multiple Centers, without the need for large number of humans to staff positions in human-in-the-loop simulations. After adding computational speed enhancements via distributed processing, NASA's FACET can be used as the prediction engine for what-if analyses, instead of the Hybrid Traffic Flow model. Thus, the proposed technology could enable a FACET-based TFM what-if analysis DST, the only required capabilities, external to FACET, would be the user interface and BN models, which can be integrated as wrappers around FACET.
A direct post application for the proposed technology is as a what-if analysis DST to be used at the FAA ATCSCC, the FAA Centers and at airline Flight Operation Centers (FOCs) for supporting NAS-wide what-if analyses while planning and negotiating potential TMI actions under a Collaborative Decision Making (CDM) operational paradigm. An ideal FAA enhancement effort, where this proposed TFM DST capability can reside, is the Strategic Flow Management Application (SFMA). Moreover, BNs based prediction of likely future scenarios can be further expanded to other flight-domains in aviation (e.g., surface-terminal-en route traffic prediction, passenger movement prediction, aircraft turnaround time prediction, safety precursor detection under uncertain pilot/controller intent) and outside aviation (e.g., road traffic prediction, aircraft engine health monitoring, monitoring pilot actions for safety assurance). Another application is as a post-operations evaluation tool for the FAA, the airlines, and international ANSPs. In this application the users operate the what-if analysis platform using historical data on the last day of operations to playback the operations by making appropriate manipulations to the actual implemented decisions (e.g., model a GDP with a higher program rate than the one actually implemented) to understand whether they could have managed the operations better as compared to what they actually did. More »
A direct post application for the proposed technology is as a what-if analysis DST to be used at the FAA ATCSCC, the FAA Centers and at airline Flight Operation Centers (FOCs) for supporting NAS-wide what-if analyses while planning and negotiating potential TMI actions under a Collaborative Decision Making (CDM) operational paradigm. An ideal FAA enhancement effort, where this proposed TFM DST capability can reside, is the Strategic Flow Management Application (SFMA). Moreover, BNs based prediction of likely future scenarios can be further expanded to other flight-domains in aviation (e.g., surface-terminal-en route traffic prediction, passenger movement prediction, aircraft turnaround time prediction, safety precursor detection under uncertain pilot/controller intent) and outside aviation (e.g., road traffic prediction, aircraft engine health monitoring, monitoring pilot actions for safety assurance). Another application is as a post-operations evaluation tool for the FAA, the airlines, and international ANSPs. In this application the users operate the what-if analysis platform using historical data on the last day of operations to playback the operations by making appropriate manipulations to the actual implemented decisions (e.g., model a GDP with a higher program rate than the one actually implemented) to understand whether they could have managed the operations better as compared to what they actually did. More »
Project Library
Primary U.S. Work Locations and Key Partners
| Organizations Performing Work | Role | Type | Location |
|---|---|---|---|
| ATAC | Lead Organization | Industry | Santa Clara, California |
Ames Research Center
(ARC)
|
Supporting Organization | NASA Center | Moffett Field, California |
Primary U.S. Work Locations
-
California
Suggest an Edit
Recommend changes and additions to this project record.