Simulation of the Separating Crowd Behavior in a T-Shaped Channel Based on the Social Force Model

Abstract

The separating behavior defines the division of a crowd from a single flow into two distributary flows due to the different pedestrians' destinations. Nevertheless, in the existing literature on pedestrian flow, there is a lack of simulation research on the separating crowd behavior in a T-shaped channel. By conducting a series of controlled experiments, we analyzed the moving trajectories and the spatial and temporal distribution characteristics of pedestrians in the separation process. Based on an analysis of the controlled experiments, we proposed an improved social force model that fully considers the characteristics of pedestrians' swapping locations, and refines the directions of pedestrians' expected speeds in three stages of the pedestrian separation process. During the simulation, we applied the improved model to explore the effects of the pedestrians' swapping locations on the macroscopic phenomena, microscopic individual behavior, and traffic efficiency within a T-shaped channel. The simulation results show that if pedestrians' swapping locations are concentrated in a certain area close to the entrance, the traffic efficiency in the T-shaped channel will be higher than that if the pedestrians' swapping locations are dispersed. Moreover, as the flow rate at the entrance increases, the swapping location becomes more concentrated closer to the entrance, the mean speed increases, and fewer conflicts occur between the pedestrians.

Occurrence mechanism and coping paths of accidents of highly aggregated tourist crowds based on system dynamics

“…Many researchers have suggested using different techniques to detect crowd behavior, such as using video cameras[7,21,27], a tracklet-based model[28], a hybrid tracking model and a GSLM-based neural network[29], the particle entropy[30], and energy models[31], an Air Patrol Robot[32], an entropic path-integral model[33,34], and so on. In addition, a few researchers have analyzed the crowd behavior under different circumstances, such as during an earthquake evacuation[35], in a t-shaped channel[36], in a football stadium[37] and during high-stress evacuations[38].…”

Direction-Decision Learning Based Pedestrian Flow Behavior Investigation

“…If the pedestrians touch each other, r ij − d ij > 0, then g(x) = x, otherwise, g(x) = 0. n ij is the normalized vector pointing from pedestrian j to i. Similarly, the repulsive force from neighboring walls can be defined as: (19) where d iw is the distance between pedestrian i and wall w. n iw is the normalized vector pointing from wall w to pedestrian i. In summary, the pedestrian can be regarded as an agent who is driven by the force…”

“…Therefore, the most popular continuum model is the SFM. The SFM was proposed by Helbing [18] to simulate crowd evacuation and selforganization, and then the extended or modified SFM was developed by considering many more real-life factors [19]. These extensions can be found in [20].…”

Simulation of Pedestrian Evacuation in University Canteen Based on Cellular Automata

“…Thus, a better understanding of the dynamics of pedestrian evacuation is needed to improve the safety and evacuation efficiency. Simulation is recognized as effective method for pedestrian dynamic analysis [5]- [9]. Being easily repeated, it can ideally capture the pedestrian dynamic process while saving manpower and material resources.…”