Abstract
Cellular Automata was applied to model the pedestrian flow, where the local neighbor and transition rules implemented to each pedestrian 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 maximum velocity of each pedestrian was expressed by introducing probability of migration to the proceeding cell. The effect of the change of sight area caused by the density of crowd on the learning process was also studied. This paper shows that the proposed method is effective to simulate the pedestrian flow on the passage.
