| NSF Org: |
ECCS Division of Electrical, Communications and Cyber Systems |
| Recipient: |
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| Initial Amendment Date: | January 22, 2008 |
| Latest Amendment Date: | January 22, 2008 |
| Award Number: | 0745237 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Radhakisan Baheti
ECCS Division of Electrical, Communications and Cyber Systems ENG Directorate for Engineering |
| Start Date: | February 1, 2008 |
| End Date: | January 31, 2014 (Estimated) |
| Total Intended Award Amount: | $400,000.00 |
| Total Awarded Amount to Date: | $400,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
77 MASSACHUSETTS AVE CAMBRIDGE MA US 02139-4301 (617)253-1000 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
77 MASSACHUSETTS AVE CAMBRIDGE MA US 02139-4301 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | EPCN-Energy-Power-Ctrl-Netwrks |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
CAREER: PRACTICAL ALGORITHMS FOR NEXT GENERATION AIR TRANSPORTATION
SYSTEMS
The core insight in this proposal is that by analyzing the large amounts of weather and airline data, we can
(1) use weather forecasts to determine schedules that are robust to uncertainty, (2) design market-based mechanisms that manage airline competition for scarce resources, and (3) incorporate environmental considerations into our optimization framework.
Intellectual merit:
This proposal simultaneously addresses three challenges: robustness, competing entities and
environmental concerns. Doing so will improve on the status quo, leading to novel optimization
algorithms and market-based mechanisms that can handle increasing air traffic loads. Understanding
weather phenomena and their effect on operations will allow better strategic and tactical control of air traffic. Equitable resource allocation algorithms will encourage truthful reporting of operational data by the airlines, provide incentives for information sharing, and improve passenger experience. Green air traffic management algorithms will support increased air traffic loads with a tolerable environmental impact.
Broader impact:
The results of this research will offer a more efficient, robust and safe air transportation system by
accommodating regulatory constraints, and shape air transportation policy. Collaborations with MIT Lincoln Laboratory and NASA Ames Research Center will enable the implementation of the results of this research through the development of decision support tools for air traffic controllers. The education plan includes the development of a new MIT course on optimization and control techniques applied to infrastructure systems. The plan also includes the development of interactive web-based materials to demystify air traffic control to the general public, and to educate K-12 students on an important engineering challenge.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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