Multidestination Pedestrian Flows in Equilibrium: A Cellular Automaton-Based Approach

This article presents a new simulation approach for multidestination pedestrian crowds in complex environments. The work covers two major topics. In the first part, a novel cellular automaton (CA) model is proposed. The model describes the pedestrian movement by a set of simple rules and produces fundamental diagrams similar to those derived from laboratory experiments. The second topic of this work describes how the CA can be integrated into an iterative learning cycle where the individual pedestrian can adapt travel plans based on experiences from previous iterations. Depending on the setup, the overall travel behavior moves either toward a Nash equilibrium or the system optimum. The functional interaction of the CA with the iterative learning approach is demonstrated on a set of transport paradoxes. Furthermore, time series of speed and density observed in a small-scale experiment show a general agreement between the CA simulation and laboratory experiments. The scalability of the proposed approach is demonstrated on a large-scale scenario.