  Predicting the behaviors of pedestrian crowds is of critical importance for a
variety of real-world problems. Data driven modeling, which aims to learn the
mathematical models from observed data, is a promising tool to construct models
that can make accurate predictions of such systems. In this work, we present a
data-driven modeling approach based on the ODE-Net framework, for constructing
continuous-time models of crowd dynamics. We discuss some challenging issues in
applying the ODE-Net method to such problems, which are primarily associated
with the dimensionality of the underlying crowd system, and we propose to
address these issues by incorporating the social-force concept in the ODE-Net
framework. Finally application examples are provided to demonstrate the
performance of the proposed method.
