@misc{ade38e03771d4a228e71cf8221d5ada1,
title = "A Decision Support Tool for Planning Neighborhood-Scale Deployment of Low-Speed Shared Automated Shuttles",
abstract = "Increasing interest and investment in connected, automated, and electric vehicles, and mobility-as-a-service concepts are paving the way for the next major shift in transportation through automated and shared mobility. The initial excitement towards rapid deployment and adoption of automated vehicles has subsided, and low-speed automated shuttles are emerging as a more pragmatic pathway for introducing automated mobility in geo-fenced districts. Such shuttles hold the promise to provide a viable alternative for serving short trips in urban districts with high travel densities. As interest in low-speed automated shuttle systems (to improve urban mobility) increases, the need for tools that can inform communities regarding benefits or dis-benefits of automated shuttle deployments is imminent. However, most of the existing transportation planning and simulation tools are not capable of handling emerging shared automated mobility options. This presentation presents a microscopic simulation toolkit that can be used by cities and communities to plan for the deployment of low-speed automated shuttles systems, as well as other shared mobility options. Labeled as the Automated Mobility District (AMD) modeling and simulation toolkit, the proposed decision support tool can help cities evaluate the mobility and sustainability impacts of deploying shared automated vehicles in geofenced regions. This paper presents a description of the toolkit, as well as a sample scenario analysis for the deployment of low-speed automated shuttles in Greenville, South Carolina. Results from the scenario study demonstrate the effectiveness of the proposed simulation toolkit in planning for advanced mobility systems.",
keywords = "automated mobility, automated mobility district, autonomous vehicles, low-speed automated shuttles, shared mobility",
author = "Lei Zhu and Venu Garikapati and Stanley Young and Jinghui Wang",
year = "2020",
language = "American English",
series = "Presented at the 2020 Transportation Research Board (TRB) 99th Annual Meeting, 12-16 January 2020, Washington, D.C.",
type = "Other",
}