Smart card data offers an in-depth understanding of travel behaviour of public transport users. An efficient way to analyze public transport users is to group them into different clusters of similar behaviour. However, this clustering process should take into account space and time, because both of these dimensions characterize daily trips. Depending on the outcome, we might wish to give more importance to space or to time, or we might wish to balance the two. In this study, we present a spatiotemporal clustering tool that permits modulation regarding to the importance of space versus time. We then test this tool with different values for the space-time balance parameter to evaluate the influence of this parameter on the results. The method has been applied to 769,614 smart card transactions of the Réseau de transport de la Capitale (Quebec City, Canada). Results show that the influence of space and time can indeed be controlled, and that the types of clusters obtained vary whether one or both of the dimensions are considered.