Book contents
- Beyond the Algorithm
- Beyond the Algorithm
- Copyright page
- Contents
- Figures
- Tables
- Foreword
- Acknowledgments
- Introduction
- 1 The Rise and Scope of Gig Work Regulation
- 2 An Uber Ambivalence
- 3 Invisible Work, Visible Workers
- 4 The Importance of Qualitative Research Approaches to Gig Economy Taxation
- 5 Just a Gig?
- 6 Algorithmic Management, Employment, and the Self in Gig Work
- 7 Regulating Transportation Systems without Authority (or Data)
- 8 Words Matter
- 9 Rewriting the Rules
- 10 What Regulators Could Gain by Listening to Rideshare Drivers
- Index
- References
7 - Regulating Transportation Systems without Authority (or Data)
Plugging an Uber- and Lyft-Sized Hole in City Transportation Planning and Policy
Published online by Cambridge University Press: 22 October 2020
- Beyond the Algorithm
- Beyond the Algorithm
- Copyright page
- Contents
- Figures
- Tables
- Foreword
- Acknowledgments
- Introduction
- 1 The Rise and Scope of Gig Work Regulation
- 2 An Uber Ambivalence
- 3 Invisible Work, Visible Workers
- 4 The Importance of Qualitative Research Approaches to Gig Economy Taxation
- 5 Just a Gig?
- 6 Algorithmic Management, Employment, and the Self in Gig Work
- 7 Regulating Transportation Systems without Authority (or Data)
- 8 Words Matter
- 9 Rewriting the Rules
- 10 What Regulators Could Gain by Listening to Rideshare Drivers
- Index
- References
Summary
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.
- Type
- Chapter
- Information
- Beyond the AlgorithmQualitative Insights for Gig Work Regulation, pp. 146 - 168Publisher: Cambridge University PressPrint publication year: 2020