Learning from big and small data for transportation planning and resilience analysis

POSTPONED – TO BE RESCHEDULED

COVID has exacerbated two emerging trends in transportation analysis: (1) the rise of passively-generated big data; and (2) the increasing need to deal with the “unexpected” disruptions. This talk emphasizes the need for learning big and small data for transportation planning and resilience analysis. Different ways of learning are described, with applications ranging from long-term planning analysis to rapid responses under disruptions.

PRESENTER 

Cynthia Chen is a professor in the Department of Civil & Environmental Engineering at the University of Washington (Seattle). She is also a professor and the interim chair of the Department of Industrial & Systems Engineering at UW. She is an internationally renowned scholar in transportation science and directs the THINK (Transportation-Human Interaction and Network Knowledge) lab at the UW. Cynthia has published over 60 peer-reviewed publications in leading journals in transportation and systems engineering including Transportation Research Part A-F and Omega, as well as interdisciplinary journals such as PNAS. Her research has been supported by federal agencies such as NSF, NIH, APAR-E, NIST, USDOT, and FHWA as well as state and regional agencies. Cynthia served a two-year assignment (2017-19) as the Program Director of Civil Infrastructure Systems, CMMI (Civil, Mechanical, and Manufacturing Innovation) division with the National Science Foundation. She is an associate director of TOMNET (Center for Teaching Old Models New Tricks), a USDOT-funded Tier 1 University Transportation Center led by ASU, as well as a key member of the new Center of Understanding Future Travel Behavior and Demand, a USDOT-funded national center led by UT Austin. Currently, Cynthia serves as an associate editor for Transportation Science, and is on the editorial board of Sustainability Analytics and Modeling.