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Advances in information technologies and vehicle automation have birthed new transportation services, including shared autonomous vehicles (SAVs). Shared autonomous vehicles are on-demand self-driving taxis, with flexible routes and schedules, able to replace personal vehicles for many trips in the near future. The siting and density of pick-up and drop-off (PUDO) points for SAVs, much like bus stops, can be key in planning SAV fleet operations, since PUDOs impact SAV demand, route choices, passenger wait times, and network congestion. Unlike traditional human-driven taxis and ride-hailing vehicles like Lyft and Uber, SAVs are unlikely to engage in quasi-legal procedures, like double parking or fire hydrant pick-ups. In congested settings, like central business districts (CBD) or airport curbs, SAVs and others will not be allowed to pick up and drop off passengers wherever they like. This paper uses an agent-based simulation to model the impact of different PUDO locations and densities in the Austin, Texas CBD, where land values are highest and curb spaces are coveted. In this paper 18 scenarios were tested, varying PUDO density, fleet size and fare price. The results show that for a given fare price and fleet size, PUDO spacing (e.g., one block vs. three blocks) has significant impact on ridership, vehicle-miles travelled, vehicle occupancy, and revenue. A good fleet size to serve the region’s 80 core square miles is 4000 SAVs, charging a $1 fare per mile of travel distance, and with PUDOs spaced three blocks of distance apart from each other in the CBD. 

Understanding the preferences for new and future transportation technologies is important to ensure an efficient and equitable future transportation system. A survey was conducted of Americans’ preferences for several such technologies. Americans are concerned about vehicle range and charging station availability for electric vehicles (EVs) and hesitant about autonomous vehicle (AV) safety. Opinions about many transportation technologies, such as vertical takeoff and landing (i.e., air taxis), shared parking, and air-drone delivery are mixed. These less familiar technologies require continued tracking of preferences. A 55% increase is estimated in the probability of an individual choosing a battery electric vehicle (BEV) pickup truck if its fuel economy increases by about 9%. This result supports a market for BEV pickup trucks currently under development by many automakers. The preference for vehicle autonomation appears to depend on the use case. Driving task automation is preferred by residents of low-density, car-dependent areas where long commutes are common. In contrast, automated parking technologies are favored by those living in denser communities. Intermittent bus lanes are favored by those living in high population density areas, but not among those in areas with high shares of zero-vehicle households. These results provide indications of where to direct future research in the field.