Socially-integrated robust communication and information-resource sharing technologies for post-disaster community self-reliance
Lead PI:
Cynthia Chen

When a disaster strikes, communities often become isolated and citizens come together to help each other: people share resources, pass along information, and take on tasks that are outside of their usual domains. These activities have been reported in both academic literature and anecdotal documents. Some examples include: New Yorkers’ sharing private vehicles and boats during the 2005 MTA strike that crippled NYC transit services; and neighbors helping neighbors escape from flooding caused by 2016’s Hurricane Matthew using rafts improvised from inflatable mattresses in Rowland, NC. These peer-to-peer resource sharing activities fill important gaps during times of disaster that cannot be fulfilled by emergency response agencies. This project helps fill this gap by working with urban and rural, higher- and lower-income communities in Washington State to understand and advance more effective local information and resource dissemination during a disaster. The project integrates hardware, robust communication technologies, social capacities, and spatial conditions to leverage and enhance place-based social networks for information-resource sharing; and investigate a little-studied scientific frontier intersecting communications, sharing, and disaster resilience. The results of the project will be scalable and useful on a daily and emergency basis to communities that increasingly face natural disaster risks and are interested in enhancing their resilience through information and resource sharing.

More specifically, the project will co-design with the two communities and conduct research in four thrusts. Thrust 1—robust communications, will develop robust off-grid community-based networks that are owned and operated by the community. Thrust 2—building community social networks for information-resource sharing, will conduct surveys to collect information about people’s sharing behavior and their social ties within a community, and develop novel models to infer community-based social networks. Thrust 3—community-based information-resource sharing, will develop models and strategies that will lead to efficient information-resource sharing. The key hypothesis is that social network structures affect the optimality and stability of information-resource sharing. Thrust 4 (Dynamic Map your Neighborhood) integrates the results of Thrusts 1-3 and uses them to co-design and pilot-test applications with two communities in WA (urban and rural). The diverse team expertise facilitates knowledge and methods across disciplines through the design of robust communication technologies, and novel ways to solicit social ties information and information-resource sharing models that considers both model optimality and human inputs (e.g., leader nominations from the communities).

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.

Cynthia Chen
Bio: Cynthia Chen is a professor in the Department of Civil & Environmental Engineering at the University of Washington (Seattle). She is an internationally renowned scholar in transportation science and directs the THINK (Transportation-Human Interaction and Network Knowledge) lab at the UW. Cynthia has published numerous peer-reviewed publications in leading journals in transportation and systems engineering including Transportation Research Part A-F and PNAS. Her research has been supported by many federal and state agencies. She is an associate director of TOMNET (Center for Teaching Old Models New Tricks), a USDOT-funded Tier 1 University Transportation Center led by ASU, as well as a co-investigator of the new Center of Understanding Future Travel Behavior and Demand, a USDOT-funded national center led by UT Austin. Currently, Cynthia is an associate editor for Transportation Science, and is on the editorial board of Sustainability Analytics and Modeling.
Performance Period: 10/01/2023 - 09/30/2027
Institution: University of Washington
Award Number: 2311405