RIAM: Measuring the Nonlinear Impact of Transportation Infrastructure in an Uncertain World (只有英文版本)
研究院/研究中心讲座
摘要
In an era of growing environmental uncertainty and social inequality, traditional linear models are increasingly inadequate for evaluating transportation infrastructure investments. This talk highlights how advanced AI and machine learning techniques reveal nonlinear, threshold-based dynamics often missed by conventional approaches. Drawing on three recent studies, I demonstrate how these methods uncover tipping points in inland waterways, assess equity impacts of infrastructure spending in U.S. cities, and evaluate the nonlinear effects of multimodal transport on city attractiveness in China. The findings highlight the need for data-driven, adaptive, and equitable frameworks in future infrastructure planning.
讲者
Prof. Zhenhua Chen
Associate Professor
City and Regional Planning, The Ohio State University, USA
Dr. Zhenhua Chen is an Associate Professor of City and Regional Planning at The Ohio State University. He has held visiting scholar positions at the University of Cambridge, Waseda University, and the Asian Development Bank. He is a board member and president-elect of the International Association for China Planning (IACP). His research focuses on infrastructure planning, disaster impact assessment, and resilience. Dr. Chen has authored three books, co-edited two volumes, and published around 100 articles in leading journals such as Transportation Research Part A, Part D, Transport Reviews, and Environment Research Letters. He was named to Stanford’s 2024 Global Top 2% Scientists List, ranking 98th in the logistics and transportation subfield. His work has been recognized with awards including the Lumley Research Award, the Geoffrey Hewings Award in regional science, the William Miernyk Research Medal, and the Charles Tiebout Prize. His research and insights have been featured in mainstream media outlets such as The Washington Post, Wall Street Journal Video Network, and Forbes.
