Abstract

Given the emergence and growth of ridesourcing companies, the forthcoming introduction of fully- autonomous vehicles (AVs), and the projected positive impact of AVs on the growth of mobility services, this paper aims to analyze the operational efficiency of two on-demand AV-enabled mobility services (AVeMSs). Specifically, the study compares an AV-enabled shared-ride service, with an AV-enabled traditional ridesourcing (i.e. no shared-rides) service, in terms of handling demand surges, when fleet size is fixed. The authors hypothesize that shared-ride service will significantly outperform traditional ridesourcing service because as demand increases with shared-ride service, the number of feasible shared-ride opportunities increases, effectively increasing the service rate of the shared-ride fleet. To test this hypothesis, the authors employ a dynamic agent-based simulation of travelers, AVs, and an AV fleet operator. The underlying AV fleet control problem is highly-dynamic and stochastic, as traveler requests are unknown to the fleet operator a priori. To solve the dynamic and stochastic optimization problem, the AV fleet operator repeatedly re-solves an online AV-traveler assignment problem based on the current state the system. The simulation results illustrate that under various experimental settings, shared-ride service significantly outperforms ridesourcing service in terms of handling demand surges. At low demand levels, traveler wait times are similar for shared-ride and ridesourcing services. However, as demand increases, average traveler wait times increase more rapidly under ridesourcing service. The results suggest shared-ride service allows fleet operators to better handle demand surges.