A simulation of crowd behavior with decision making process model

Abstract

In recent years, pedestrian flow modeling, simulation and optimization has received a surge in attention. There are two reasons for investigating pedestrian flow. One is to solve the day-to-day problems of crowded public spaces, such as stations, shopping malls, pavements and many other places. The other is to optimize evacuation procedures from densely occupied buildings and urban centers in case of an unpredicted event. Such evacuation planning can save many lives at sudden catastrophic events like tsunami. As walking is the most basic mode of transportation, many pedestrian models have been proposed. However, finding a realistic description of pedestrian behavior is extremely challenging and pedestrian modeling still remains a matter of debate. For this, the elaboration of a model is necessary. This research proposes a microscopic pedestrian simulation model based on concepts of cognitive science. In this model, the pedestrian utilizes visual cues as primary source of information to decide where to walk. The pedestrian movement is generated from bottom-up by describing behavioral heuristics and motion control process. It is clearly demonstrated by numerical analyses that this model reproduces human-like individual trajectories and facilitates smooth pedestrian traffic flows through the effective avoidance maneuvers. Our approach has potential for contribution toward investigating disaster risk reduction strategies in complex urban space.