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Data obtained from a serious game was used to estimate a game-based model to better understand user preferences regarding shared automated vehicles (SAVs), while modeling how user experience and social interactions can shape decisions over time. Users’ choice of three novel, fully automated transportations modes; shared ride, shared car, and automated transit, was studied with ten interacting players engaged in a competitive mode choice game. The players aimed to maximize their overall score, which was affected by their mode choice, their punctuality, and the choices of all the other players. Each game had 50 rounds to allow implicit learning and provide insight on how choices are shaped with experience. Results showed a strong shift to shared rides over shared cars and transit with road users reaching a state of equilibrium in terms of scores. The implications of game-based methods on formation of user preferences and mode choice is discussed.