Learning from experimental data to simulate pedestrian dynamics

Accuracy is a critical concern for those engaged in the simulation of pedestrian dynamics. Although a range of pedestrian models has been developed, the performance of these models is heavily limited by simplifications inherent in the usual knowledge-based approach. In this study, we propose data-driven models of pedestrian dynamics based on the machine learning approaches. The models are constructed in an iterative manner; thus, the long-term future trajectories are predicted using the current output as the input at the next timestep. The performance of our models is evaluated by comparison with an well-researched knowledge-based pedestrian model. The evaluation included two metrics: the ADE/FDE evaluation metric and another metric that measures whether the simulation corresponded to ground truths from the collective dynamics perspective. Experiments on two experimental datasets show that our models outperform the knowledge-based model. Our findings highlight the considerable potential of the data-driven approach for handling pedestrian simulation tasks.