| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | May 24, 2021 |
| Latest Amendment Date: | May 24, 2021 |
| Award Number: | 2133960 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Daan Liang
dliang@nsf.gov (703)292-2441 CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | October 1, 2020 |
| End Date: | September 30, 2022 (Estimated) |
| Total Intended Award Amount: | $89,995.00 |
| Total Awarded Amount to Date: | $72,395.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
6823 SAINT CHARLES AVE NEW ORLEANS LA US 70118-5665 (504)865-4000 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
6823 St. Charles Avenue New Orleans LA US 70118-5698 |
| 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): | HDBE-Humans, Disasters, and th |
| 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
This project develops new methods to forecast real-time population behavior during natural disasters, potentially transforming the current state of emergency response in a cost-effective way. To understand how individuals, infrastructure systems, and emergency services should prepare and respond during such disasters, this project utilizes data available from multiple sources including from transportation systems and online social media. Using innovative data science approaches to integrate data from multiple sources increases the quality of the data available for emergency response prediction and improved evacuation traffic management. Research outputs will be shared with the practitioner community to facilitate improved decision making for emergency agencies in hurricane evacuation and disaster management. This scientific research contribution thus supports NSF's mission to promote the progress of science and to advance our national welfare. In this case, the benefits will be insights to improve emergency response, which will save lives, economic losses, and reduce panic, anger and confusion during a future event.
The project combines heterogeneous data sources from transportation systems and social media, in a unified framework-providing better information for modeling dynamic population behavior during hurricanes. To accurately predict evacuation demand, this project leverages large-scale real-time data, rarely used by existing emergency decision support tools. It advances the data science of disaster management by developing novel information fusion techniques to represent population and its behavior while employing government survey and social media data, text-mining approaches to extract evacuation intent from social media data, and evacuation traffic prediction models to optimize transportation resources. Through its innovative data gathering and modeling approaches, this project will enhance our ability to deal with future hurricanes. The project engages a broader participation of graduate and undergraduate students including from under-represented groups and plans a broader dissemination of results to traffic engineers and emergency management officials from local counties and cities.
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project developed new methods to forecast real-time population behavior during natural disasters, enhancing emergency response approaches in a cost-effective way. To understand how individuals, infrastructure systems, and emergency services should prepare and respond during such disasters, this project utilized data from multiple sources including from transportation systems, hurricane forecasts, and online social media. Using innovative data science approaches to integrate data from multiple sources increases the quality of the data available for emergency response prediction and improved evacuation traffic management.
This project developed several technical innovations, including: (a) methodologies for identifying and understanding expressions of evacuation intent on social media; (b) approaches to design forecasting models that can be tuned on past data but still easily be applied to new hurricanes; (c) models that can combine information from social media, traffic patterns, and hurricane forecasts into a cohesive system. These innovation contribute to numerous fields, including natural language processing, social media analysis, transportation engineering, and emergency management. The results of this project were shared via annual community workshops with the practitioner community to facilitate improved decision making in emergency response operations.
From an educational perspective, the project enabled cross-disciplinary training of graduate and undergraduate students in computer science, transportation engineering, and emergency management.
Last Modified: 08/11/2023
Modified by: Aron Culotta
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