Simulating bi-directional pedestrian flow in a cellular automaton model considering the body-turning behavior

2017 ◽  
Vol 482 ◽  
pp. 666-681 ◽  
Author(s):  
Cheng-Jie Jin ◽  
Rui Jiang ◽  
Jun-Lin Yin ◽  
Li-Yun Dong ◽  
Dawei Li
Keyword(s):  
Cellular Automaton ◽  
Cellular Automaton Model ◽  
The Body ◽  
Pedestrian Flow ◽  
Turning Behavior

Simulating pedestrian flow by an improved two-process cellular automaton model

2017 ◽  
Vol 28 (02) ◽  
pp. 1750016 ◽  
Author(s):  
Cheng-Jie Jin ◽  
Wei Wang ◽  
Rui Jiang ◽  
Li-Yun Dong
Keyword(s):  
Cellular Automaton ◽  
Critical Density ◽  
Maximum Flow ◽  
Cellular Automaton Model ◽  
Open Boundary ◽  
Flow Density ◽  
Open Boundary Conditions ◽  
Pedestrian Flow ◽  
Exchange Rule ◽  
Lane Formation

In this paper, we study the pedestrian flow with an Improved Two-Process (ITP) cellular automaton model, which is originally proposed by Blue and Adler. Simulations of pedestrian counterflow have been conducted, under both periodic and open boundary conditions. The lane formation phenomenon has been reproduced without using the place exchange rule. We also present and discuss the flow-density and velocity-density relationships of both uni-directional flow and counterflow. By the comparison with the Blue-Adler model, we find the ITP model has higher values of maximum flow, critical density and completely jammed density under different conditions.

Simulation study of overtaking in pedestrian flow using floor field cellular automaton model

2017 ◽  
Vol 28 (05) ◽  
pp. 1750059 ◽  
Author(s):  
Zhijian Fu ◽  
Liang Xia ◽  
Hongtai Yang ◽  
Xiaobo Liu ◽  
Jian Ma ◽  
...  
Keyword(s):  
Cellular Automaton ◽  
Normal State ◽  
Past Research ◽  
Cellular Automaton Model ◽  
Pedestrian Flow ◽  
Fundamental Diagram ◽  
Tactical Decision ◽  
Floor Field ◽  
Walking Environment ◽  
Moving Path

Properties of pedestrian may change along the moving path, for example, as a result of fatigue or injury, which has never been properly investigated in the past research. The paper attempts to study tactical overtaking in pedestrian flow. That is difficult to be modeled using a microscopic discrete model because of the complexity of the detailed overtaking behavior, and crossing/overlaps of pedestrian routes. Thus, a multi-velocity floor field cellular automaton model explaining the detailed psychical process of overtaking decision was proposed. Pedestrian can be either in normal state or in tactical overtaking state. Without tactical decision, pedestrians in normal state are driven by the floor field. Pedestrians make their tactical overtaking decisions by evaluating the walking environment around the overtaking route (the average velocity and density around the route, visual field of pedestrian) and obstructing conditions (the distance and velocity difference between the overtaking pedestrian and the obstructing pedestrian). The effects of tactical overtaking ratio, free velocity dispersion, and visual range on fundamental diagram, conflict density, and successful overtaking ratio were explored. Besides, the sensitivity analysis of the route factor relative intensity was performed.

scholarly journals Optimising Pedestrian Flow Around Large Stadiums

2021 ◽  
Vol 6 ◽  
Author(s):  
Yuming Dong ◽  
Xiaolu Jia ◽  
Daichi Yanagisawa ◽  
Katsuhiro Nishinari
Keyword(s):  
Differential Evolution ◽  
Cellular Automaton ◽  
Differential Evolution Algorithm ◽  
Cellular Automaton Model ◽  
Pedestrian Flow ◽  
Evacuation Time ◽  
Specific Strategy ◽  
Evolution Algorithm ◽  
Surrounding Areas ◽  
The Individual

This study proposes a method that combines the cellular automaton model and the differential evolution algorithm for optimising pedestrian flow around large stadiums. A miniature version of a large stadium and its surrounding areas is constructed via the cellular automaton model. Special mechanisms are applied to influence the behaviour of an agent that leaves from a certain stadium gate. The agent may be attracted to a nearby business facility and/or guided to uncongested areas. The differential evolution algorithm is then used to determine the optimal probabilities of the influencing agents for each stadium gate. The main goal is to reduce the evacuation time, and other goals such as reducing the costs for the influencing agents’ behaviours and the individual evacuation time are also considered. We found that, although they worked differently in different scenarios, the attraction and guidance of agents significantly reduced the evacuation time. The optimal evacuation time was achieved with moderate attraction to the business facilities and strong guidance to the detouring route. The results demonstrate that the proposed method can provide a goal-dependent, exit-specific strategy that is otherwise hard to acquire for optimising pedestrian flow.

A CELLULAR AUTOMATON MODEL FOR COLLECTIVE MOTION OF MICROORGANISMS

2007 ◽  
Vol 40 (9) ◽  
pp. 161-166
Author(s):  
Yasushi Iwatani ◽  
Koichi Hashimoto ◽  
Yuki Deguchi
Keyword(s):  
Cellular Automaton ◽  
Collective Motion ◽  
Cellular Automaton Model
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