Development of a Model to Evaluate Policy Approaches for Transportation Network Companies and the Private Vehicle to Address Congestion and Promoting Equity in San Francisco

Research Lead: Pravin Varaiya

UC Campus(es): UC Berkeley

Problem Statement: Transportation Network Companies (TNCs) like Uber and Lyft are popular with the public and growing in use. Unfortunately, TNCs have disrupted traffic and increased congestion in San Francisco. Between 2010 and 2016, daily TNC vehicle miles traveled (VMT) increased by 630,000 miles, accounting for 51% of increased delay and 25% of total delay. With 45,000 drivers, TNCs are the city’s largest employer. These drivers earn below the $15 hourly minimum wage and they are beginning to protest their working conditions. The combination of high value to the customer, inferior working conditions for the employees, and negative congestion externalities has prompted cities to consider TNC regulation, including New York City and London, the two largest Uber markets.

Project Description: The research team evaluates the impact of three proposed regulations of transportation network companies (TNCs) like Uber, Lyft and Didi: (1) A minimum wage for drivers, (2) a cap on the number of drivers or vehicles, and (3) a per-trip congestion tax. The impact is assessed using a queuing theoretic equilibrium model which incorporates the stochastic dynamics of the app-based ride-hailing matching platform, the ride prices and driver wages established by the platform, and the incentives of passengers and drivers. The research team shows that a floor placed under driver earnings can push the ride-hailing platform to hire more drivers and offer more rides, at the same time that passengers enjoy faster rides and lower total cost, while platform rents are reduced. They also construct variants of the model to briefly discuss platform subsidy, platform competition, and autonomous vehicles.

Status: Completed

Budget: $70,000