Seminar title: 

The charm of machine learning approaches in addressing nonlinear relationships between variables

 

Abstract: Empirical studies often assume a (generalized) linear relationship between variables.  However, scholars need to diagnose this assumption.  If the true relationship is nonlinear, the linear assumption will lead to a biased estimate.  This presentation emphasizes the charm of machine learning approaches in address the nonlinear relationships between variables in the context of land use and transportation.  The case studies show that the applications of machine learning may change the conventional understanding of land use-transportation interactions and inform planners of efficient solutions to address transportation-related challenges. 

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Speaker bio: 

Dr. Jason Cao specializes in land use and transportation interaction, the effects of ICT on travel behavior, and planning for quality of life.  He has published more than 120 peer-reviewed papers and edited four books.  He is internationally well-known for his research on residential self-selection in the relationships between the built environment and travel behavior.  His recent work focuses on the applications of machine learning approaches in addressing the nonlinear relationships in land use and transportation research.  He received his degrees from University of California, Davis and Tsinghua University.