High-Statistics Modeling of Complex Pedestrian Avoidance Scenarios

Abstract

Modeling the behavior of pedestrians walking in crowds is an outstanding fundamental challenge, deeply connected with the physics of flowing active matter. The strong societal relevance of the topic, for its relations with individual safety and comfort, sparked vast modeling efforts from multiple scientific communities. Yet, likely because of the technical difficulties in acquiring experimental data, models quantitatively reproducing (statistical) features of pedestrian flows are scarce. This contribution has a twofold aim. First, we consider a pedestrian dynamics modeling approach previously proposed by some of the authors and based on Langevin equations. We review the approach and show that in the undisturbed and in the pair-wise avoidance regimes (i.e., in absence of interactions between pedestrians and in case of avoidance of a single individual walking in the opposite direction) the model is in quantitative agreement with real-life high-statistics measurements. Second, moving towards the final goal of quantitative and generic crowd dynamics models, we consider the more complex case of a single individual walking through a dense crowd advancing in the opposite direction. We analyze the challenges connected to treating such dynamics and extend the Langevin model to reproduce quantitatively selected observed features.