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Predicting the behaviors of pedestrian crowds is of critical importance for avariety of real-world problems. Data driven modeling, which aims to learn themathematical models from observed data, is a promising tool to construct modelsthat can make accurate predictions of such systems. In this work, we present adata-driven modeling approach based on the ODE-Net framework, for constructingcontinuous-time models of crowd dynamics. We discuss some challenging issues inapplying the ODE-Net method to such problems, which are primarily associatedwith the dimensionality of the underlying crowd system, and we propose toaddress these issues by incorporating the social-force concept in the ODE-Netframework. Finally application examples are provided to demonstrate theperformance of the proposed method.

ODEs learn to walk: ODE-Net based data-driven modeling for crowd dynamics