GAMESlab

Serious games (SGs) are virtual systems that allow the reconstruction of the laws governing the behavior of complex adaptive systems such as urban transportation and social interaction. Unlike stated preference-based studies, improved visualization, feedback, and scores mediate players’ learning through experience. SG’s potential to understand users’ preferences regarding shared automated vehicles (SAVs) is developed. The investigation focused on three innovative, entirely automated commuting options: shared rides, shared cars, and automated transit. The research involved 10 participants actively involved in a competitive mode selection exercise, which emulated 50 workdays and was conducted in 10 separate sessions. The players aimed to maximize their overall score influenced by their mode choice, punctuality, and the other players’ choices. SG-obtained data was used to estimate a game-based discrete choice model. The sustainability policy implications of game-based methods on the future adoption of SAVs and impacts on other modes are further discussed.