On the Operational Feasibility of Autonomous Vehicle Crowdsourcing Service under Heterogeneous Supply Availability

Abstract

Autonomous vehicle (AV) crowdsourcing service enables privately-owned AVs to rent out their vehicles to serve other travelers during idle periods. Existing literature suggests that this business model holds potential benefits for travelers, AV owners, and the operator, yet the analysis has mostly overlooked the heterogeneity in the spatiotemporal supply pattern. Motivated by this gap, we propose a time-expanded network model that jointly optimizes the AV rental price and vehicle dispatching, where the private AV supply is characterized by the idle periods and available locations of different classes of AV owners. With the proposed model, we numerically investigate the operational feasibility of an AV crowdsourcing service and identify key factors that influence service performance using a stylized two-region model. We further conduct a case study of Chicago based on the household travel survey and ride-hailing trip data. The results not only offer practical insights for service operators but also provide regulators with a better understanding of the potential impact of AV crowdsourcing on the citywide travel patterns and traffic dynamics.