Despite growing interest in low-speed automated shuttles, pilot deployments have only just begun in a few places in the U.S., and there is a lack of studies that estimate the impacts of a widespread deployment of automated shuttles designed to supplement existing transit networks. This project estimated the potential impacts of automated shuttles based on a deployment scenario generated for a sample geographic area: Santa Clara County, California. The project identified sample deployment markets within Santa Clara County using a GIS screening exercise; tested the mode share changes of an automated shuttle deployment scenario using BEAM, an open-source beta software developed at the Lawrence Berkeley National Laboratory to run traffic simulations with MATSim; elaborated the model outputs within the R environment; and then estimated the related impacts. The main findings have been that the BEAM software, despite still being in its beta version, was able to model a scenario with the automated shuttle service: this report illustrates the potential of the software and the lessons learned. Regarding transportation aspects, the model estimated automated shuttle use throughout the county, with a higher rate of use in the downtown San José area. The shuttles would be preferred mainly by people who had been using gasoline-powered ride hail vehicles for A-to-B trips or going to the bus stop, as well as walking trips and a few car trips directed to public transport stops. As a result, the shuttles contributed to a small decrease in emissions of air pollutants, provided a competitive solution for short trips, and increased the overall use of the public transport system. The shuttles also presented a solution for short night trips—mainly between midnight and 2 am—when there are not many options for moving between points A and B. The conclusion is that the automated shuttle service is a good solution in certain contexts and can increase public transit ridership overall.
Preliminary photointerpretation and damage maps of landslide and other surficial deposits in northeastern San Jose, Santa Clara County, California