PFI:BIC: CityWarn -A Smart, Hyperlocal, Context-Aware Hazard Notification Service System.

University Of Massachusetts Amherst, Amherst MA

Investigators

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).