Ride-Sharing Networks with Mixed Autonomy

Abstract

We consider ride-sharing networks served by human-driven vehicles and autonomous vehicles. First, we propose a novel model for ride-sharing in this mixed autonomy setting for a multi-location network in which the platform sets prices for riders, compensation for drivers, and operates autonomous vehicles for a fixed price. Then we study the possible benefits, in the form of increased profits, to the ride-sharing platform that are possible by introducing autonomous vehicles. We first establish a nonconvex optimization problem characterizing the optimal profits for a network operating at a steady-state equilibrium and then propose a convex problem with the same optimal profits that allows for efficient computation. Next, we study the relative mix of autonomous and human-driven vehicles that results at equilibrium for various costs of operation for autonomous vehicles. In particular, we show that there is a regime for which the platform will choose to mix autonomous and human-driven vehicles in order to optimize profits. Our results provide insights into how such ride-sharing platforms might choose to integrate autonomous vehicles into their fleet.