Purdue University, led by Prof. Satish Ukkusuri along with researchers from Virginia Tech and North Dakota State University, have been selected to receive a $2.5 million National Science Foundation Hazards SEES grant to understand the role of uncertainty in hurricane evacuation decision-making.

This interdisciplinary project will develop next-generation data-driven tools for capturing and mitigating uncertainty in hazards such as hurricanes. Recent hurricanes demonstrate the importance of developing new scientific methods in area of hazards. Hurricane Sandy is estimated to have caused more than $70 billion in losses in the NY-NJ region while Hurricane Katrina caused significant loss of life; and Hurricanes Katrina and Rita caused traffic chaos. This project will develop data-driven modeling, social science and computational systems science approaches leveraging recent advancements in data gathering which ultimately improve the safety of evacuations. The outcomes from this project will assist emergency managers and agencies to anticipate transportation and sheltering needs, and enhance community planning prior to and after hurricanes. These improvements will lead to lower evacuation costs, stress, and loss of life.

The multi-university team is a collaboration of top researchers from Purdue (Satish Ukkusuri from Civil Engineering, Seungyoon Lee from Communication and Milind Kulkarni from Electrical and Computer Engineering), North Dakota State University (Yue Ge and Daniel Klenow from Emergency Management) and Virginia Tech (Pamela Murray-Tuite from Civil Engineering). The research will promote the integration of household behaviors with traffic simulation leading to better policies that will translate into safer evacuations and saving lives.

The project involves collecting novel data through post-hurricane mail surveys, personal interviews, web experiments, social media, and process tracking software and developing new integrative scientific approaches to modeling household level behaviors and social network effects across households and other stakeholders. This project will develop new understanding of household level decision-making behaviors, how individuals and agencies process uncertainty at different instances of the hurricane onset, and the consequences of the household decisions on city-scale traffic congestion, using computational sciences as a supporting discipline. In addition, this project will model evacuation logistics for hurricanes. The project will provide a holistic approach to characterize, measure, and analyze uncertainty in various aspects of hurricane evacuation modeling, social networks, household decision making, and stochastic traffic modeling.

Additional details about the project can be found at: http://www.nsf.gov/awardsearch/showAward?AWD_ID=1520338