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Mobile phone companies routinely record the location and communication patterns of millions of cellular phone users, maintaining a detailed record of human activity patterns. These cell
phone users form a large pre-existing mobile wireless sensor net, generating datasets of potential value to public safety managers, emergency response personnel, traffic engineers, city planning and resource
management, offering a thorough snapshot of what humans do on a daily basis, how crowds self-organize, and how individuals alter their behavior when faced with emergencies, traffic jams, riots, political protests or
terrorist attacks.
This project will develop an integrated Wireless Phone Based Emergency Response System (WIPER) that is capable of real-time monitoring of normal social and geographical communication and
activity patterns of millions of wireless phone users, recognizing unusual human agglomerations, potential emergencies and traffic jams. WIPER will select from these massive data streams high-resolution information
in the physical vicinity of a communication or traffic anomaly, and dynamically inject it into an agent-based simulation system to classify and predict the unfolding of the emergency in real time. The agent-based
simulation system will dynamically steer local data collection in the vicinity of the anomaly. Multiple distributed data collection, monitoring, analysis, simulation and decision support modules will be integrated
using a Service Oriented Architecture (SOA) to generate traffic forecasts and emergency alerts for engineering, public safety and emergency response personnel.
This multidisciplinary project will involve
researchers from physics, the social sciences, and computer science. In addition, experts in social networks, panic development and agent-based simulations in urban environments will contribute to the project.
A news article about the WIPER project can be found here.
The material presented at this web site is based in part upon work supported by the National Science Foundation, the DDDAS Program, under Grant No. CNS-050348.
Any
opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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