Multi-floor Evacuation Model Based on Wavelet Neural Network

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

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.

Key words: multi-floor, crowd evacuation, social force model, field, wavelet neural network

CLC Number:

X913

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.

Figures/Tables 11

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