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The concept of automated cars is rapidly becoming a reality. Yet there has been very little analysis of the impacts of such developments on the performance of urban transport systems. These impacts are potentially complex. On the positive side, automation has the potential to increase road capacity, make driving available to more people, and reduce accidents and emissions. On the negative side, it could attract users away from public transport, walking and cycling, substantially increase traffic levels and stimulate urban sprawl. These impacts cannot currently be measured empirically and, by the time that they can, it will be too late to change the implementation model to rectify any resulting problems. Predictive assessments are therefore needed. This paper considers the possible impacts of automated vehicles, predicts their effects on the urban land use and transport system, and discusses the policy implications. We focus on automation of the car fleet, and do not consider the potential of automation of public transport or freight. We consider the literature on the range of attributes of automated vehicles which might affect transport and land use patterns, and suggest potential outcomes for each over the period to 2050. These include the proportion of automated vehicles in the car fleet, whether automated vehicles are privately purchased or publicly shared, the impacts on network capacity, the reduced need to pay for and walk from parking places, the potential reduction in the value of in-vehicle time and the potential use by current non-drivers. We represent these attributes in an expanded causal link diagram of the urban land use and transport system and import those causal links into the MARS system dynamics model. We determine from the literature a level for each attribute, and test the impacts in a set of ten scenarios using an updated MARS model of Leeds. Based on our input assumptions, we find that car-km in 2050 could be over 50% higher than in the business as usual scenario. Public transport use could fall by 18%, threatening accessibility for those dependent on it, while walking and cycling could fall by 13%, reducing their health benefits. Overall person-km would rise, suggesting a reduction in sustainability. A requirement that all automated cars are shared vehicles could reduce these adverse impacts somewhat, but the effects are sensitive to the charge per km. Our use of a single value for each attribute means that our analysis is exploratory, but the size of the resulting impacts demonstrates the importance of understanding the scale of systems response to each of the attributes which we have considered. It will be important to manage the way in which automated cars are introduced into urban areas, if they are not to lead to a worsening of the urban environment, accessibility and health. A requirement to make all such vehicles part of shared fleets offers one way forward, but more work is needed to understand the way in which use of such fleets should be charged. • We review the literature on the possible demand-side and supply-side impacts of autonomous automobiles on urban transport and land use under private and shared ownership, and on the likely scale of such impacts. • We enhance a System Dynamics Land Use – Transport Interaction Model, MARS, to reflect the causal effects of the majority of these impacts. • We apply the enhanced model to the city of Leeds, UK, to test the effects of each potential impact on its own and of all impacts combined, under both fully private and fully shared ownership. • We find a clear synergistic effect between the impacts, leading to considerably higher levels of automobile use under automation; we predict car-km to increase by 56% for private ownership and 41% under shared ownership by 2050 when compared with business as usual. Total person-km rise by 39% and 32% respectively, while use of public transport, walking and cycling fall, indicating a move to a less sustainable future. • We explore the impacts on land use, suggesting that, while private ownership will add to urban sprawl, shared ownership might have the opposite effect. We discuss the policy implications. [ABSTRACT FROM AUTHOR]