A Learning Algorithm for the Simulation of Pedestrian Flow by Cellular Automata

Cellular Automata was applied to model the pedestrian flow, where the local neighbor and transition rules implemented to each person in the crowd were determined automatically by the experience of pedestrians. The experience was based on two parameters; the number of continuous vacant cells in front of the cell to proceed, and the number of pedestrian in the cell to proceed. The experience was evaluated numerically, and a pedestrian selected the cell to proceed by the evaluated index. The flow formations by pedestrians in the opposite direction on a straight pathway and on a corner were simulated, and the number of rows was discussed in relation to the density of pedestrian on the simulation space.