This grant will develop theoretical foundations and empirically test the feasibility of a novel framework for real-time monitoring and disruption prediction of road transportation networks during extreme events at a road-segment level of granularity. Recent natural disasters have shown that road transportation networks mainly fail due to unexpected gridlocks resulting from unusual traffic patterns. These disruptions adversely affect emergency management processes such as evacuations, rescue, and recovery operations. Prompt prediction of disruptions is vital for a successful emergency management system, but despite this critical need, emergency management processes still lack real-time network monitoring and prediction methods for transportation systems. Real-time monitoring and disruption prediction of road networks creates opportunities to develop proactive emergency management systems that lead to efficient, fast, and successful rescue and recovery operations. The benefits of more efficient and successful emergency management operations will be passed down to the public and will result in enhanced quality of life, health, and well-being for commuters and other infrastructure users. Ultimately, this project will contribute to developing sustainable and resilient cities and communities that can function properly even under the stress of extreme events. In connection with this project, educational and outreach efforts are envisioned for integration into undergraduate and graduate courses. This grant will provide an opportunity for project-based learning for K-12, undergraduate, and graduate students, especially minorities and under-represented groups.
This research project aims to conduct fundamental research to create a methodology and framework that dynamically predicts disruptions in road transportation networks based on unusual traffic patterns detected during extreme events. The method designed in this project offers a holistic network approach with a road segment level of granularity that, while it considers the entire network, is also able to monitor and predict traffic flow in each road segment. The specific research objectives are to 1) discover and model temporal traffic flow interdependencies, 2) efficiently monitor traffic flows on a real-time basis at a road segment level of granularity using instantaneous traffic data to detect unusual traffic patterns, and 3) predict disruptions by integrating network interdependencies and the identified unusual traffic patterns. The outcomes of this project will set the stage for development of proactive emergency management systems that will result in more efficient and successful rescue and recovery operations.
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.
