Curb Staging: Understanding the Impacts of Automated Vehicle Ridehail Fleet Operations Under Different Parking Policies

Automated vehicle (AV) ridehailing services are now operating in several metropolitan regions in the United States. While providing benefits, AV ridehailing services may exacerbate issues related to curb usage and vehicle kilometers traveled (VKT) in urban areas. The objective of this study is to provide guidance to cities by evaluating the impacts of AVs' short-term curb usage for staging between serving ride requests. We focus on the following performance metrics: VKT, curb productivity, customer wait time, and customer matching rate. To perform the analysis, we use a high-fidelity commercial simulation tool that models the dynamics of AV ridehailing fleet operations. We use high-quality, high-resolution data, including synthetic ridehailing trip data and forecasts of curb availability. Our baseline scenario includes a hypothetical fleet size of 1,700 vehicles serving a hypothetical total of 68,000 daily trips in San Francisco, California. We construct scenarios that vary in terms of whether, where, and when AVs can stage at curbs, as well as whether AVs strategically reposition to high-demand areas. We also vary the day of the week. According to our simulation results, excluding AVs from staging at the curb would increase daily VKT by over 200,000 kilometers or nearly 60 percent, compared to scenarios where AVs can stage at the curb. In a separate analysis, we find that prohibiting curb staging in residential areas and on curbsides with metered parking would increase empty VKT by 5.4%. We also present key performance metrics that are both temporally and spatially resolved, providing additional policy-relevant information. The simulation modeling is supplemented by expert interviews (n=14) with practitioners, regulators, and policymakers in the fields of curbside management and innovative mobility to gain additional insight on policy considerations related to AV curb access, staging, and parking.