SBIR/STTR Research Award
SBIR Phase I:Simulating Demand and Competition for Emerging Transportation Modes
Award amount
$268,580Program and phase
SBIR · Phase IAward year
2023Award details
- Awarding agency
- National Science Foundation
- Branch
- Not reported
- Recipient company
- ATLANTA ANALYTICS LLC
- Proposal award date
- 2023-04-01
- Contract end date
- 2023-12-31
- UEI
- NHUXQKUEQMR8
- Solicitation number
- NSF 22-551
- Topic code
- MO
Research abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to advance the state-of-the-art for demand modeling for electric aircraft by developing a decision support tool that assesses the broad economic, environmental, and equity impacts that these new modes of transportation will have on society. The project is motivated by recent advancements in battery technologies, autonomy, and distributed propulsion that are transforming air transportation through the development of a new class of electric vertical takeoff and landing (eVTOL) aircraft. The development of electric aircraft for longer distances is also in progress and could revitalize and support new service out of regional airports.The decision support tool can be used by government agencies, aircraft manufacturers, and other stakeholders to guide policy and multi-million dollar public and private infrastructure investments in a way that achieves a balance between economic, environmental, and equity impacts._x000D_
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This SBIR Phase I project will push the frontiers in modeling demand for new transportation modes, simultaneously addressing competition with ground modes, competitive pricing, and stimulated demand.The project focuses on examining how to model competition between air and ground transportation for distances between 50 and 350 miles where air travel has not been economically feasible in the past.This project focuses on three unproven, high-impact innovations. The first is the design of a scalable, demand simulation platform for air travel that includes distances in which the new aircraft are expected to operate. The second is the development of algorithms that use location-based data to identify the movement of current passengers between airport pairs and to predict demand stimulation. The third is the use of machine learning to calibrate air transportation networks for competitive marketplaces that have hundreds of inputs._x000D_
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This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Technology keywords
Not reported