Assessing the operational performance of ridesharing-enabled shared autonomous vehicles fleets

Shared autonomous vehicles (SAVs) are quickly spreading in major cities and may become a preferredmobility solution in the near future. Dynamic ridesharing (DRS) is envisioned to enhancethe performance of SAV systems by increasing vehicle occupancy and lowering empty vehicle kilometrestravelled (VKT). Nonetheless, existing literature assesses the impact of DRS-SAV utilisingunrealistic traffic models or heuristic matching and pricing methods. To address this gap, thispaper presents a simulation-based service design assessment framework to test real-time SAV operationstrategies. Travellers’ mode choices are explicitly modelled, and advanced DRS operationstrategies, involving optimal matching and pricing, are tested in a mixed-traffic urban network.The results indicate that advanced DRS methods and accounting for travellers’ mode choicesgreatly increase the number of served travellers with even smaller VKT, while incurring only aslight increase in waiting and travel time. If properly managed, DRS can significantly reduce thecongestion caused by private trips and empty VKT.