Hazards SEES: Bridging Information, Uncertainty, and Decision-Making in Hurricanes using an Interdisciplinary Perspective
11/2015 - 10/2019 (Estimated)
NSF - National Science Foundation
Satish Ukkusuri, Purdue University (Principal Investigator)
Pamela Murray-Tuite (Co-Principal Investigator)
Seungyoon Lee, (Co-Principal Investigator)
Milind Kulkarni (Co-Principal Investigator)
Yue Ge (Co-Principal Investigator)
In order to respond more effectively to hurricanes, emergency managers need better decision support tools that account for the response of populations to uncertainty in hurricane forecasts, as well as the consequences of household decisions on key infrastructure systems. For example, Hurricane Sandy is estimated to have caused more than $70 billion in losses, and Hurricane Katrina caused significant loss of life; in both cases, the situation was made worse due to chaos in key transportation systems. We are addressing this need by developing next generation data-driven tools for capturing and mitigating uncertainty in hazards such as hurricanes. Using data from various sources, we are developing new understanding of household level behaviors, how individuals and agencies process uncertainty at different instances of the hurricane onset, and the consequences of the household decisions on citywide traffic congestion. We are developing data-driven modeling, social science, and computational systems science approaches leveraging recent advancements in data gathering in order to improve the effectiveness of evacuations and save lives.
We are collecting novel data through post-hurricane mail surveys, personal interviews, web experiments, social media, and process tracing software and developing new integrative scientific approaches to modeling household level behaviors and social network effects across households and other stakeholders. Using these data and methods, we are modeling evacuation logistics for hurricanes, using computational sciences as a supporting discipline. We are thus providing 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. The advances in knowledge from this project will impact multiple disciplines including emergency management, complex systems science, transportation engineering, and computational sciences. Our research outcomes will assist emergency managers and agencies to anticipate transportation and sheltering needs and to improve community planning prior to hurricanes by integrating household behavior information with traffic simulation. These improvements will lead to safer and more effective evacuations at lower cost, reduced stress, and most importantly, with less loss of life.