Multi-floor Evacuation Model Based on Wavelet Neural Network

Authors

Juan Wei, 1.School of Computer Science, Chengdu Normal University, Chengdu 611130, China;2.Key Laboratory of Interior Layout Optimization and Security, Institutions of Higher Education of Sichuan Province, Chengdu Normal University, Chengdu 611130, China;Follow
Lei You, 3.College of Computer Science, Chengdu University, Chengdu 610106, China;
Yangyong Guo, 1.School of Computer Science, Chengdu Normal University, Chengdu 611130, China;2.Key Laboratory of Interior Layout Optimization and Security, Institutions of Higher Education of Sichuan Province, Chengdu Normal University, Chengdu 611130, China;
Zhihai Tang, 1.School of Computer Science, Chengdu Normal University, Chengdu 611130, China;

Abstract

Abstract: Crowd evacuation in a multi-floor environment is a popular social concern, while the stagnation phenomenon easily occurs when simulating a multi-floor complex environment with the traditional social force model. Therefore, An improved social force model is proposed by a wavelet neural network, and a new multi-floor evacuation model is built. In the model, a pedestrian's direction of movement is obtained by the field model, which is used as the self-driving direction of the social force model. Meanwhile, the evaluation indexes of the exit congestion degree, path congestion degree, and average velocity in a multi-floor environment are given, and a wavelet neural network is employed to develop an evacuation optimization method. The evacuation process is simulated by the platform and the improved model, and the key factors in this model are studied. The results show that properly increasing the evacuation velocity of pedestrians can improve evacuation efficiency, but if the velocity is too high, pedestrians will gather in the corridor quickly, which is not conducive to evacuation. In addition, the evacuation time shows a decreasing trend with the increase in the staircase width before becoming stable, and when the staircase width reaches 8 m, further growth of the staircase width will not reduce the evacuation time.

Recommended Citation

Wei, Juan; You, Lei; Guo, Yangyong; and Tang, Zhihai (2022) "Multi-floor Evacuation Model Based on Wavelet Neural Network," Journal of System Simulation: Vol. 34: Iss. 2, Article 9.
DOI: 10.16182/j.issn1004731x.joss.20-0718
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss2/9

First Page

269

Revised Date

2020-12-21

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.20-0718

Last Page

277

CLC

X913

Recommended Citation

Juan Wei, Lei You, Yangyong Guo, Zhihai Tang. Multi-floor Evacuation Model Based on Wavelet Neural Network[J]. Journal of System Simulation, 2022, 34(2): 269-277.

DOI

10.16182/j.issn1004731x.joss.20-0718