Modeling the Mutual Anticipation in Human Crowds With Attention Distractions

Abstract

Human crowds exhibit rich self-organizing behaviors through local interactions. The understanding of interaction mechanisms has important implications for the management of large-scale crowds. Although most vision-based heuristic models are successful, some features such as distracted pedestrians, and empirical phenomena like sudden turns are difficult to be explained. Here, a heuristic interaction model is proposed, which incorporates the extracted laws of pedestrian heterogeneity and mutual anticipation. We argue that pedestrians are heterogeneous in terms of speed and attention (e.g., distracted by cell phones), and have anticipations for other pedestrians’ velocities during the interaction. Numerical simulations indicate that our model realistically simulates the self-organizing phenomenon in the “distraction experiment,” along with related experimental findings. The “freezing-by-heating” phenomenon, as well as interesting phenomena such as “sidewalk shuffling” are successfully predicted, as exhibited in empirical observations. Taken together, our model may serve various potential fields involving the control of traffic flows and navigation of autonomous swarm robots.