There is little empirical work on the economic and environmental impacts of parking management policies in the U.S. This is largely due to scarce data on parking, including information on types of parking management policies for on- and off-street parking throughout a city. However, recently, cities and mobile applications have begun to leverage sensors and collect parking data. One of the more prominent programs to implement and utilize this relatively new technology is SFpark in San Francisco (SF). The purpose of SFpark is to use demand-responsive pricing to improve parking availability by updating metered and garage prices based off information on parking occupancy. The goal of this research proposal is to understand the relationships among economic and environmental outcomes resulting from SFpark using regression analysis, and of particular interest are three aspects of SFpark: 1) increasing parking availability (a priority goal of SFpark), 2) utilizing variable-priced parking, and 3) implementing metered parking in areas where or during times when it was not used before.
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