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

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).

2018 ◽  
Vol 7 (2) ◽  
pp. 79 ◽  
Author(s):  
Lin Huang ◽  
Jianhua Gong ◽  
Wenhang Li ◽  
Tao Xu ◽  
Shen Shen ◽  
...  

Author(s):  
S. M. P. Siddharth ◽  
P. Vedagiri

The design of pedestrian sidewalks depends on pedestrian flow, which is related to the speed of pedestrians on the sidewalk. The social force model (SFM) is a microscopic pedestrian simulation model that has been able to reproduce many self-organization phenomena of pedestrian flow such as lane formation. Studies have shown that the SFM has been modified to model particular pedestrian behaviors in different situations by introducing new forces or introducing new factors in existing forces. Also, the literature shows that pedestrian speed varies because of pedestrian characteristics such as age, gender, group behavior, and so forth. There are no studies that model the effect of these pedestrian characteristics using the SFM. Therefore, in this study, we have modeled the effect of gender of pedestrians by introducing a gender factor [Formula: see text]. A sidewalk in Mumbai, India has been chosen for this study. Pedestrian flow and speed were collected from the site. A base SFM containing the driving force, pedestrian–pedestrian interaction force, and pedestrian–boundary interaction force was coded in MATLAB. This model contains six parameters, which were calibrated using a genetic algorithm. Next, the SFM was modified to include different reaction times for the male and female pedestrians, [Formula: see text] and [Formula: see text], respectively. Keeping other parameters as constant, [Formula: see text] and [Formula: see text] were calibrated and found. Gender factors [Formula: see text] and [Formula: see text] are found by dividing the reaction time [Formula: see text] and [Formula: see text] by [Formula: see text], respectively. These gender factors could be found for the different male/female composition of pedestrians, which would help in analyzing the level of service of sidewalks.


2020 ◽  
Vol 121 ◽  
pp. 42-53 ◽  
Author(s):  
I.M. Sticco ◽  
G.A. Frank ◽  
F.E. Cornes ◽  
C.O. Dorso

2018 ◽  
Vol 34 ◽  
pp. 91-98 ◽  
Author(s):  
Charitha Dias ◽  
Hiroaki Nishiuchi ◽  
Satoshi Hyoudo ◽  
Tomoyuki Todoroki

2019 ◽  
pp. 477-486
Author(s):  
Yufei Yuan ◽  
Bernat Goñi-Ros ◽  
Tim P. van Oijen ◽  
Winnie Daamen ◽  
Serge P. Hoogendoorn

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