Dataset: Automated Vehicles and Central Business District Parking: the Effects of Drop-off-Travel on Traffic Flow and Vehicle Emissions

The potential for automated vehicles (AVs) to reduce parking to allow for the conversion of on-and off-street parking to new uses, such as new space for walk, bike, and shared -micro-mobility services, and housing), has sparked significant interest among urban planners. AVs could drop-off and pick-up passengers in areas where parking costs are high or limited. Personal AVs could return home or park in less expensive locations and shared AVs could serve other passengers. However, reduced demand for parking would be accompanied by increased demand for curbside drop-off/pick-up space with related movements to enter and exit the flow of traffic. This change could be particularly challenging for traffic flow in downtown urban areas during peak hours when high volumes of drop-offs and pick-ups events are likely to occur. Only limited research examines the travel and greenhouse gas effects (GHG) of a shift from parking to drop-off/pick-up travel and the effects of changes in parking supply. Our study uses a microscopic road traffic model with local travel activity data to simulate vehicle travel in San Francisco’s downtown central business district to explore traffic flow, VMT, and GHG effects of AV scenarios in which we vary (1) the demand for drop-off and pick-up travel versus parking, (2) the supply of on-street and off-street parking, and (3) the change in demand for parking and drop-off/pick-up travel due to a significant change in price of using curbside space.