Obstacle avoidance in the improved social force model based on ant colony optimization during pedestrian evacuation

Obstacle avoidance behavior, as an important part of pedestrian evacuation, is of great significance to the study of evacuation efficiency. This paper investigates the definitions of desired direction in the social force model for obstacle avoidance of two types of pedestrians, including pedestrians with and without complete evacuation information. Ant colony optimization algorithm is adopted to navigate pedestrians with complete information. Herding behavior, individual preference affected by obstacles and walls are taken into consideration when defining the desired direction of pedestrians with local information. Simulation experiments are carried out to explore obstacle avoidance dynamics, the effects of herding behavior and visibility. Results indicate that pedestrians with complete information can not only avoid obstacles better, but also have the ability to choose the shorter route to the exit which could improve the leaving efficiency. Meanwhile, the trajectories of pedestrians with local information are always accompanied by some twists and turns, which could obviously lead to a waste of time. No matter whether the proportion of pedestrians with complete information is large or not, herding behavior can make the trajectories of pedestrians with local information smoother, and the individual behavior can make their trajectories more curved. Moreover, the larger the visibility radius is, the smoother trajectories become and the greater effective displacements for pedestrians with local information are. (C) 2021 Published by Elsevier B.V.