In this talk, I will discuss a general statistical inference problem built on a network structure, with a special application in transportation. The general problem is defined as: how can one infer global network parameters (x) based on data measured on local parameters (y), with the relation between x and y built on a complex network structure? A familiar example of such problem in transportation is origin-destination (O-D) matrix estimation based on road sensor data such as traffic counts. With more traffic data become available through advanced information technologies, we face great opportunities as well as challenges in utilizing the data. In this talk, I will show how optimization and statistics could be combined to address challenges brought by data uncertainty and heterogeneous information types.
Dr. Fan is a professor in Civil and Environmental Engineering at University of California, Davis. She is also a faculty member in graduate programs of Applied Mathematics and Business Analytics. She received her PhD in Civil Engineering at University of Southern California in 2003. Dr. Fan’s research is on transportation and energy infrastructure systems modeling, with special interest in integrating applied mathematics and engineering domain knowledge to address challenges brought by system uncertainty, dynamics, and indeterminacy issues.