| NSF Org: |
TI Translational Impacts |
| Recipient: |
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| Initial Amendment Date: | August 29, 2016 |
| Latest Amendment Date: | April 6, 2020 |
| Award Number: | 1632193 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Jesus Soriano Molla
jsoriano@nsf.gov (703)292-7795 TI Translational Impacts TIP Directorate for Technology, Innovation, and Partnerships |
| Start Date: | September 1, 2016 |
| End Date: | August 31, 2021 (Estimated) |
| Total Intended Award Amount: | $999,880.00 |
| Total Awarded Amount to Date: | $1,037,624.00 |
| Funds Obligated to Date: |
FY 2018 = $34,344.00 FY 2020 = $3,400.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
101 COMMONWEALTH AVE AMHERST MA US 01003-9252 (413)545-0698 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
MA US 01003-9242 |
| 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): |
GOALI-Grnt Opp Acad Lia wIndus, PFI-Partnrships for Innovation, IIS Special Projects |
| Primary Program Source: |
01001819DB NSF RESEARCH & RELATED ACTIVIT 01002021DB NSF RESEARCH & RELATED ACTIVIT |
| 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): | 47.084 |
ABSTRACT
This project proposes to develop next generation warning systems that will improve how people make decisions about hazardous weather, such as thunderstorms, tornados and floods. Over the past two decades, cities have become a locus of population and economic activity. Currently, over 80% of the US population is concentrated in cities; furthermore, 80% of the Gross Domestic Product in the United States is produced in metropolitan areas. The concentration of people and economic activity makes cities even more vulnerable to extreme weather events. Given these trends, effective hazardous weather warning systems are critical. Hazard warning systems are service systems that aim to minimize deaths, injuries, property loss, infrastructure destruction, and service or business disruption. They include the sensors, forecasts, networking and communications, public safety personnel and decision-makers, warning information, and those who receive and respond to the warnings.
CityWarn addresses three important issues for hazard notification service systems: 1) Coordination and sharing. Public safety agencies, private sector firms and the general public, all have their own hazard warning needs, and over the years, sector-specific, and even hazard-specific warning systems have evolved that may not share important information in efficient and useful ways. 2) Data Explosion. There's an explosion of data from all sources, from fine scale meteorological observations and traffic data, to humans reporting weather on social media. This is challenging for decision-makers who must quickly make sense of all of this information; and 3) Smart phone penetration. There is now a proliferation of smart phones, plus a trend toward hyperlocal, user-selected information. Warning systems have the potential to personalize weather warnings in a way that can make warning response more effective.
CityWarn will deliver user-defined, dynamically changing alerts through a next-generation communications and networking platform. The platform is linked to a cutting edge radar system that provides high-resolution weather information on an urban scale of streets and neighborhoods. A mobile app delivers user-configured, weather information. Our integrated research will focus on Computing & Sensing, Behavioral Sciences, Engineered Systems and Testbeds. The Computing & Sensing thrust will develop new scalability, security, and functional advances within the communication and networking platform, and integrate high-resolution radar products and user-generated observations from the field. Through cognitive task analysis, usability studies, and live experiments, behavioral science researchers will learn how fieldworkers, such as utility workers, police, firefighters, stormwater personnel, use and share weather information in the context of their tasks and organizational constructs. Our engineered systems work will focus on aggregation and sharing of sensed information sources, automation of warning processes to address data overload problems, and user alert customization. By developing a common platform for use by industry and public sector players, we hope to break down silos between existing warning systems and increase inter-agency coordination and improve response time and quality. The main test bed will be a living lab in the Dallas Fort Worth metroplex where weather data will be disseminated to users during actual severe weather events.
Primary partners: University of Massachusetts, Amherst (lead): Electrical & Computer Engineering, Computer Science, Resource Economics; Colorado State University: Electrical & Computer Engineering; Oncor Electric Delivery Company (Large Business, Dallas, Texas); TruWeather Solutions (Small Business, Reston, VA). Other broader context partners are the City of Fort Worth (Government, Fort Worth, Texas);CommPower (Small Business, Camarillo, CA); and IBM (Large Business, Armonk, New York).
This award is partially supported by funds from the Directorate for Computer and Information Science and Engineering (CISE), Division of Information and Intelligent Systems (IIS).
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 PFI-BIC developed CityWarn - a hyperlocal, context-aware hazard warning system - through co-creation with industry and public safety stakeholders. CityWarn is a software platform that ingests dynamic hazard data from many sources, rapidly assesses unique threat thresholds for different locations and/or users based on their distinct needs and contexts, and delivers personalized alerts and hazard avoidance information. This information can go directly to users through a mobile app (such as the CityWarn app) or be ingested into other software or hardware. The grant initially focused on severe weather public safety applications and expanded to include Autonomous Air Mobility (AAM), focusing on weather detection and avoidance for Unpiloted Autonomous Vehicles (UAVs). During the grant period, different versions of CityWarn software have operated in the Dallas Fort Worth Metroplex, California, and upstate New York, enabling research to take place in the context of live weather events and stakeholder decision-making, or through simulations based on archived actual data.
Our team included University of Massachusetts and Colorado State University; Oncor, utility company, the City of Fort Worth, and TruWeather Solutions, a hyperlocal weather notification systems company, and Understory, a weather station manufacturer. IBM was a supporter and collaborator on this project.
Key research accomplishments in computer science, sensing, behavioral science, and systems engineering include:
- Expanded CityWarn capabilities, based on user insights, to enable replicable on-demand instantiations of the software and flexibility in serving different classes of regional users;
- Developed efficient computational workflows for extracting relevant information from massive amounts of sensor data;
- Created a simulation environment using Information Centric Networking to evaluate the optimal placement of wireless base stations for disaster communications by analyzing mobility patterns derived from the actual user movement and alerting preferences logged by the CityWarn App;
- Adapted CityWarn for UAV weather avoidance by creating new APIs for the system; developing new dynamic routing for UAVs.
- Created high-resolution forecasts of rain and hail;
- Tailored CityWarn for urban flash flood management by integrating high-resolution rainfall data, road sensors, rain gauges, vulnerable infrastructure, and querying tools for flash flood managers
- Established effective approaches for co-creation with public safety practitioners focused on case analysis and iterative system design and demonstration;
Broader impacts include:
- Demonstrated CityWarn technology for UAV weather detection , alerting and avoidance to NASA and FAA through sponsored research projects with Bell Textron and with industry partner TruWeather Solutions.
- Participated in NASA?s National AAM campaign with industry and universities, influencing standards for how weather information will be shared and used in the emerging AAM market.
- Licensed CityWarn technology to AAM weather company.
- City of Fort Worth uses CityWarn for flash flood management.
- 7 graduate students participated in the research.
Last Modified: 02/07/2022
Modified by: Brenda J Philips
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