CAREER: Data-driven Models of Human Mobility and Resilience for Decision Making

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This project envisions mobile cyber-physical systems (CPS) where people carrying cell phones generate large amounts of location information that is used to sense, compute and monitor human interactions with the physical environment during environmental dislocations. The main objective will be to identify the types of reactions populations have to a given type of shock, providing decision makers with accurate and informative data-driven representations they can use to create preparedness and response plans. Additionally, the outcomes of this project will allow for the development of tools to assess and improve the effectiveness of different types of preparedness and response policies through feedback loops in the mobile CPS. These feedback loops could show how community behaviors during shocks change when policies are re-defined based on the computations of the CPS, and vice-versa. Previous work by the PI and others has already showed that CPS integrating people and cell phones as sensing platforms can be used to collect location information at large scale and to compute, using data mining and machine learning techniques, human mobility behaviors during shocks. However, most of the results are very limited and ad-hoc, lacking any type of serious applicability from a preparedness and response policy. This project will advance the state of the art by developing accurate methods and effective tools for decision-making during shocks in mobile CPS. From a broader impacts perspective, the proposed research will contribute in two areas: (a) real-world deployments, to promote data-driven policy development, data-driven analyses of human behavior, and the use of feedback loops in mobile CPS for decision-making assessment; and (b) the creation of an educational plan and training opportunities in the areas of data science for social good and mobile CPS for decision making.The main outcomes of the project will include novel data-driven methods for mobile CPS that will reliably characterize and predict human mobility patterns and resilience during shocks so as to improve preparedness and response policies. The project will make use of cell phone metadata and social media to achieve the following three objectives: (1) to characterize the types of reactions that communities have to different kinds of shocks using real-time data from mobile CPS, which would allow for the development of more adequate preparedness policies to be ready for future events; (2) to create predictive methods to forecast the impact that shock management policies would have on human mobility behaviors and community resilience during a shock, using human behavioral information from the CPS feedback loop when different policies are applied (either in real-time or in batch processing); and (3) to evaluate the transferability of the types of reactions and predictive methods across different shocks, spatio-temporal scales and data sources in mobile CPS, which would provide decision makers with the possibility of analyzing behaviors and resilience in communities where cell phone metadata in the CPS is not fully available. From an intellectual merit perspective, the proposed methods will advance the state of the art in data analytics and real-time systems for CPS in the area of Smart and Connected 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.