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Modeling mixed-traffic motion and interactions is crucial to assess safety,efficiency, and feasibility of future urban areas. The lack of trafficregulations, diverse transport modes, and the dynamic nature of mixed-trafficzones like shared spaces make realistic modeling of such environmentschallenging. This paper focuses on the generalizability of the motion model,i.e., its ability to generate realistic behavior in different environmentalsettings, an aspect which is lacking in existing works. Specifically, our firstcontribution is a novel and systematic process of formulating general motionmodels and application of this process is to extend our Game-Theoretic SocialForce Model (GSFM) towards a general model for generating a large variety ofmotion behaviors of pedestrians and cars from different shared spaces. Oursecond contribution is to consider different motion patterns of pedestrians bycalibrating motion-related features of individual pedestrian and clusteringthem into groups. We analyze two clustering approaches. The calibration andevaluation of our model are performed on three different shared space datasets. The results indicate that our model can realistically simulate a widerange of motion behaviors and interaction scenarios, and that adding differentmotion patterns of pedestrians into our model improves its performance.

On the Generalizability of Motion Models for Road Users in Heterogeneous Shared Traffic Spaces