This Faculty Early Career Development Program (CAREER) project will establish a mathematical framework based on transportation system modeling and fusion of large-scale multi-source data across different systems including roadway, public transit and parking. The goal is to exploit the spatio-temporal characteristics of travel demand at different scales, understand how network disruption probabilistically affects the transportation systems, and help facilitate decision making regarding planning and real-time operations. The project will lay the foundation for data-driven transportation science that is to have an impact at all scales from individuals' quality of life to the nation's economy. This project will involve collaboration with several public agencies and private firms to develop, deploy and test real-world systems in the Pittsburgh metropolitan area based on large-scale data analytics. All models, algorithms, and examples will be implemented and open sourced in the public domain to promote access to applications and spark discussions from users all over the world. The results will be used to develop a new undergraduate course on data analytics for infrastructure management. Additionally, a virtual laboratory will be developed in conjunction with Carnegie Museum of Natural History for educating students in grades 7-12, college students, and the general public. Students from both Carnegie Mellon University and University of Pittsburgh will be engaged through learning sessions, data analytics competitions, and hands-on activities.
The goal of this CAREER project is to develop theories and algorithms that utilize large-scale data to infer characteristics of probabilistic network flow and optimally manage transportation networks under uncertainty. High-dimensional joint probability distributions are used to explicitly model probabilistic network flow and system states in the context of flow dynamics and user behavior. Those joint probability distributions are learned, estimated, and predicted from fusing and mining fine-grained data collected over many years. They reveal the second-order statistics of flow and system states, namely variance-covariance, resulting in a better understanding of the inter-relations among flow/system characteristics, at high temporal and spatial granularity. This project will also develop rigorous statistical theories to identify recurrent and non-recurrent flow patterns in subnetworks through dynamic network partition. The science of network optimization will be advanced by integrating probabilistic network flow into decision making theories for both planning and operation. If successful, this research creates a new paradigm for sensing, modeling, designing, planning and operating complex infrastructure networks in an efficient, holistic, timely and reliable manner.
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.
