  Modeling mixed-traffic motion and interactions is crucial to assess safety,
efficiency, and feasibility of future urban areas. The lack of traffic
regulations, diverse transport modes, and the dynamic nature of mixed-traffic
zones like shared spaces make realistic modeling of such environments
challenging. This paper focuses on the generalizability of the motion model,
i.e., its ability to generate realistic behavior in different environmental
settings, an aspect which is lacking in existing works. Specifically, our first
contribution is a novel and systematic process of formulating general motion
models and application of this process is to extend our Game-Theoretic Social
Force Model (GSFM) towards a general model for generating a large variety of
motion behaviors of pedestrians and cars from different shared spaces. Our
second contribution is to consider different motion patterns of pedestrians by
calibrating motion-related features of individual pedestrian and clustering
them into groups. We analyze two clustering approaches. The calibration and
evaluation of our model are performed on three different shared space data
sets. The results indicate that our model can realistically simulate a wide
range of motion behaviors and interaction scenarios, and that adding different
motion patterns of pedestrians into our model improves its performance.
