Behavior-based cellular automaton model for pedestrian dynamics

2017 ◽  
Vol 292 ◽  
pp. 417-424 ◽  
Author(s):  
Keke Huang ◽  
Xiaoping Zheng ◽  
Yuan Cheng ◽  
Yeqing Yang
2003 ◽  
Vol 67 (5) ◽  
Author(s):  
Ansgar Kirchner ◽  
Katsuhiro Nishinari ◽  
Andreas Schadschneider

2012 ◽  
Vol 31 ◽  
pp. 1066-1071 ◽  
Author(s):  
Ling Fu ◽  
Jixiong Luo ◽  
Minyi Deng ◽  
Lingjiang Kong ◽  
Hua Kuang

2007 ◽  
Vol 18 (06) ◽  
pp. 927-936 ◽  
Author(s):  
WENGUO WENG ◽  
HONGYONG YUAN ◽  
WEICHENG FAN ◽  
YUJI HASEMI

In this paper, experiments were carried out to validate the motor schema-based cellular automaton model for pedestrian dynamics presented by Weng et al. [Int. J. Mod. Phys. C17, 853 (2006)]. Unidirectional pedestrian flow with different walk velocities is studied by means of simulations and experiments. The lower walk velocity was provided by the young people with special equipment, which made them walk slowly, to imitate the elderly people. Travel time of each pedestrian from start line to goal line with different initial distribution patterns and obstacles in the way was measured to compare with simulation results. The comparison results indicate that the experimental observations can be well reproduced with the motor schema-based cellular automaton model. And the simulation results of the mean travel time agree well with the experimental data.


2006 ◽  
Vol 17 (06) ◽  
pp. 853-859 ◽  
Author(s):  
WENGUO WENG ◽  
YUJI HASEMI ◽  
WEICHENG FAN

A new cellular automaton model for pedestrian dynamics based on motor schema is presented. Each pedestrian is treated as an intelligent mobile robot, and motor schemas including move-to-goal, avoid-away and avoid-around drive pedestrians to interact with their environment. We investigate the phenomenon of many pedestrians with different move velocities escaping from a room. The results show that the pedestrian with high velocity have predominance in competitive evacuation, if we only consider repulsion from or avoiding around other pedestrians, and interaction with each other leads to disordered evacuation, i.e., decreased evacuation efficiency. Extensions of the model using learning algorithms for controlling pedestrians, i.e., reinforcement learning, neural network and genetic algorithms, etc. are noted.


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