The potential for automated vehicles (AVs) to reduce parking in central cities has generated much excitement among urban planners. AVs could drop-off (DO), and pick-up (PU) passengers in areas where parking costs are high: personal AVs could return home or park in less expensive locations, and shared AVs could serve other passengers. Reduced on-street and off-street parking present numerous opportunities for redevelopment that could improve the livability of cities, for example, more street and sidewalk space for pedestrians and bicycle travel. However, reduced demand for parking would be accompanied by increased demand for curbside DO/PU space with related movements to enter and exit the flow of traffic. This change could be particularly challenging for traffic flows in downtown urban areas during peak hours, where high volumes of DOs and PUs are likely to occur. Only limited research examines the travel effects of a shift from parking to DO/PU travel and the impact of changes in parking supply. This study uses a microscopic road traffic model with local travel activity data to simulate personal AV parking scenarios in San Francisco’s downtown central business district (CBD). In these scenarios, the research team varies (1) the demand for DO and PU travel versus parking, (2) the supply of on-street and off-street parking, and (3) the total demand for parking and DO/PU travel due to an increase in the cost to travel to the CBD.
