Smart Cities: Data and Decision science for parking management

Parking management has been a vexing problem for cities since the invention of the automobile. Among the concerns are traffic congestion, air pollution, and greenhouse gas emissions caused by drivers searching for available parking - an activity colloquially known as cruising. Recently, there has been a wave of interest in effective curb parking management, particularly through performance-based pricing, has arisen in cities as diverse as Seoul, Mexico City, New York, Seattle, Los Angeles, and Budapest. The movement is exemplified by San Francisco, which introduced variable priced parking to improve space availability and reduce cruising. I will present some empirical observations, stochastic models, simulation models, statistical methods and pricing optimization results for the management of parking. This work has inspired the deployment of several parking management programs around the work.

Bio:
Robert C. Hampshire is an associate professor of public policy at the Ford School, a research associate professor in both the U-M Transportation Research Institute's (UMTRI) Human Factors group and Michigan Institute for Data Science (MIDAS), and an affiliated faculty member in the Department of Industrial and Operations Engineering (IOE). He develops and applies operations research, data science and systems engineering methodologies to public and private service industries. His research focuses on the management and policy analysis of emerging innovative mobility services such as smart parking, connected vehicles, autonomous vehicles, ride-hailing, bike sharing, and car sharing. He has worked extensively with both public and private sectors partners worldwide. He is a queueing theorist that uses statistics, stochastic modeling, simulation and dynamic optimization. Hampshire received a Ph.D. in operations research and financial engineering from Princeton University.