scholarly journals Social Force Model-Based Group Behavior Simulation in Virtual Geographic Environments

2018 ◽  
Vol 7 (2) ◽  
pp. 79 ◽  
Author(s):  
Lin Huang ◽  
Jianhua Gong ◽  
Wenhang Li ◽  
Tao Xu ◽  
Shen Shen ◽  
...  
Keyword(s):  
Group Behavior ◽  
Force Model ◽  
Social Force ◽  
Social Force Model ◽  
Model Based ◽  
Behavior Simulation

scholarly journals Improved Social Force Model Based on Emotional Contagion and Evacuation Assistant

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 195989-196001
Author(s):  
Zhiyun Zheng ◽  
Guanglei Zhu ◽  
Zhenhao Sun ◽  
Zhenfei Wang ◽  
Lun Li
Keyword(s):  
Emotional Contagion ◽  
Force Model ◽  
Social Force ◽  
Social Force Model ◽  
Model Based

Modified Social Force Model Based on Predictive Collision Avoidance Considering Degree of Competitiveness

2016 ◽  
Vol 53 (1) ◽  
pp. 331-351 ◽  
Author(s):  
Yuan Gao ◽  
Tao Chen ◽  
Peter B. Luh ◽  
Hui Zhang
Keyword(s):  
Collision Avoidance ◽  
Force Model ◽  
Social Force ◽  
Social Force Model ◽  
Model Based

Improved social force model based on exit selection for microscopic pedestrian simulation in subway station

2015 ◽  
Vol 22 (11) ◽  
pp. 4490-4497 ◽  
Author(s):  
Xun Zheng ◽  
Hai-ying Li ◽  
Ling-yun Meng ◽  
Xin-yue Xu ◽  
Xu Chen
Keyword(s):  
Subway Station ◽  
Force Model ◽  
Social Force ◽  
Social Force Model ◽  
Model Based ◽  
Selection For ◽  
Pedestrian Simulation

scholarly journals An Improved Social Force Model for Bicycle Flow in Groups

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ying-Xu Rui ◽  
Tie-Qiao Tang ◽  
Jian Zhang
Keyword(s):  
Group Size ◽  
Critical Density ◽  
Group Behavior ◽  
Numerical Results ◽  
Force Model ◽  
Social Force ◽  
Social Force Model ◽  
Maximum Value ◽  
Negative Impacts ◽  
Positive Impacts

Bicycle flow widely has group behavior (i.e., cyclists have a tendency to ride in groups), which may have some significant effects on the bicycle’s motion. However, the existing studies on bicycle flow rarely consider this factor. Generally, bicycle flow has two kinds of group behaviors, i.e., shoulder group behavior and following group behavior. In this paper, we propose an improved social force (SF) model to describe the two kinds of group behaviors. Then, we use the improved SF model to, respectively, explore the effects of the two kinds of group behaviors on the bicycle’s motion from the simulation perspective. The numerical results show that (i) shoulder group behavior has some negative impacts on the bicycle’s motion, i.e., the critical density (where the through capacity can reach the maximum value), the jam density, and the through capacity will be reduced; (ii) following group behavior has some positive impacts on the bicycle’s motion, i.e., the critical density, the jam density, and the through capacity will be enhanced; (iii) the impacts of coexistence of shoulder and following group behavior are related to the density. Besides, increasing group size and group probability will enlarge the negative impacts of shoulder group behavior and alleviate the positive impacts of following group behavior. These results can guide administrators to better manage bicycle flow (especially reasonably control the negative impacts of group behaviors).

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