Ride-Sourcing Platforms with Mixed Autonomy: How will Autonomous Vehicles Affect Others on a Ride-Sourcing Network?

Abstract

This paper investigates how autonomous vehicles (AV) affect passengers, for-hire human drivers, and the platform on a ride-sourcing network. We consider a ride-sourcing market where a mixture of AVs and human drivers is deployed by the platform to provide mobility-on-demand services under labor regulations. In this market, the ride-sourcing platform determines the spatial prices, fleet size, human driver payments, and vehicle relocation strategies to maximize its profit, while individual passengers choose between different transport modes to minimize their travel costs. A market equilibrium model is proposed to capture the interactions among passengers, human drivers, AVs, and the ride-sourcing platform over the network. The overall problem is formulated as a non-convex program with network constraints, and an algorithm is developed to derive its approximate solution with performance guarantee. Our study shows that ridesourcing platform prioritizes AV deployment in high-demand areas to make a higher profit. As AVs flood into these high-demand areas, they compete with human drivers in the urban core and push them to relocate to suburbs. This leads to reduced earning opportunities for human drivers and increased spatial inequity for passengers. We also show that placing a wage floor may protect drivers from the negative impact of AVs, and meanwhile the total vehicle supply and passenger demand are almost unaffected. However, there exists a threshold beyond which the minimum wage will trigger a paradigm shift of labor supply where the platform will completely replace all human drivers with AVs. This indicates that the minimum wage should be carefully designed in the mixed environment to avoid the loss of job opportunities for human drivers. These results are validated with realistic case studies for San Francisco.