The Impact of Autonomous Vehicles on Ride-Hailing Platforms with Strategic Human Drivers

TL;DR

This paper models the integration of autonomous vehicles into ride-hailing platforms with strategic human drivers, formulating a bi-level optimization to maximize platform profit and analyzing the impact of AVs on fleet management.

Contribution

It introduces a novel bi-level optimization framework for mixed AV-CV ride-hailing systems, capturing strategic driver behavior and platform control in a unified model.

Findings

Optimal fleet dimensioning depends on supply and demand dynamics.

Heuristics like AV-first can be effective but may overlook driver reactions.

Proposed algorithms effectively solve complex non-convex optimization problems.

Abstract

Motivated by the rapid development of autonomous vehicle technology, this work focuses on the challenges of introducing them in ride-hailing platforms with conventional strategic human drivers. We consider a ride-hailing platform that operates a mixed fleet of autonomous vehicles (AVs) and conventional vehicles (CVs), where AVs are fully controlled by the platform and CVs are operated by self-interested human drivers. Each vehicle is modelled as a Markov Decision Process that maximizes long-run average reward by choosing its repositioning actions. The behavior of the CVs corresponds to a large game where agents interact through resource constraints that result in queuing delays. In our fluid model, drivers may wait in queues in the different regions when the supply of drivers tends to exceed the service demand by customers. Our primary objective is to optimize the mixed AV-CV system so that the total profit of the platform generated by AVs and CVs is maximized. To achieve that, we formulate this problem as a bi-level optimization problem OPT where the platform moves first by controlling the actions of the AVs and the demand revealed to CVs, and then the CVs react to the revealed demand by forming an equilibrium that can be characterized by the solution of a convex optimization problem. We prove several interesting structural properties of the optimal solution and analyze simple heuristics such as AV-first where we solve for the optimal dispatch of AVs without taking into account the subsequent reaction of the CVs. We propose three numerical algorithms to solve OPT which is a non-convex problem in the platform decision parameters. We evaluate their performance and use them to show some interesting trends in the optimal AV-CV fleet dimensioning when supply is exogenous and endogenous.