This chapter first provides a framework for understanding recent local government approaches to aligning Uber and Lyft operations with urban transportation policy goals—including improving street safety, improving transportation access, and reducing greenhouse gas emissions. Many of these approaches to setting policy and designing streets are not regulatory per se, though they can and have been used as de facto regulatory strategies. This “implicit” regulatory approach has arisen in part because most local governments in the U.S. lack the formal authority to regulate Uber and Lyft. Furthermore, most local governments also lack the data necessary to develop and/or enforce appropriate regulations of the app-enabled for-hire vehicle industry.

The chapter continues with a case study of how the San Francisco County Transportation Authority, in partnership with researchers at Northeastern University, developed a creative and partnership-driven approach to policy-making in the face of a severe data deficit. Agency staff and University researchers scraped data from the Uber and Lyft application programming interfaces and used those data to better understand how people move in San Francisco County. This work demonstrates the importance of innovative, goal-oriented problem-solving approaches to inform the regulation of increasingly complex city streets.