摘要
预测人员疏散时间是大规模人群管控、建筑平面布局优化的关键.现有研究关注人员疏散理论模型的建立,忽视了疏散模型的时间和空间复杂度高难以实现快速预测的这一问题.复杂建筑空间环境以及人员的空间分布是决定疏散时间长短的决定性因素,本文提出以建筑空间环境和人位置分布图像为输入的疏散时间快速计算方法.设计不同空间布局、不同疏散人数、不同出口个数及宽度的疏散场景,使用人员疏散元胞自动机模型(CA)开展模拟计算构建样本数据库.构建疏散时间快速预测模型FastEvacNet,训练标定该深度卷积神经网络模型,测试模型预测疏散时间的可靠度与效率.结果表明,在整个测试集上的MAPE值为8.21%,模型整体预测精度良好,泛化能力强.模型对疏散时间的预测用时几乎不依赖于疏散场景的复杂度,计算效率相较于CA模型提升三个数量级;当以数据流的形式传入网络进行批量预测时,可提升四个数量级.
Evacuation time prediction is of critical importance to the management and control of pedestrian crowds and optimizing building layout.Current researches focus on establishing theoretical evacuation models.However,due the high spatial-temporal complexity of the evacuation models,it is difficulty to realize rapid evacuation time prediction.The complex building space environment and the spatial distribution of pedestrians are the decisive factor that determines the length of the evacuation time.This paper proposed a fast calculation method to determine evacuation time based on the image of the building layout and the distribution of pedestrians.Evacuation scenarios considering different number of evacuees,different number of exits,different exit widths,and different space layout forms have been designed.Then,with a cellular automata evacuation model(CA),simulations have been performed to build an evacuation database.Next,a fast evacuation time prediction model,namely,FastEvaNet,has been proposed.This deep convolutional neural network model was then trained and tested with the simulation database.Then,the reliability and efficiency of evacuation time prediction model has been evaluated.The results show that the MAPE value is 8.21%.The overall prediction accuracy of the model is good,and the generalization ability is strong.What is more,the computational time of evacuation time is almost independent of the complexity of the evacuation scenario.The computational efficiency is three orders of magnitude higher than that of the CA model.When the data flow is transmitted to the network for batch prediction,the efficiency can be four orders of magnitude higher.
作者
赵思琪
陈娟
王巧
夏钤强
卢紫嫣
马剑
ZHAO Siqi;CHEN Juan;WANG Qiao;XIA Qianqiang;Jacqueline T.Y.Lo;MA Jian(School of Transportation and Logistics,Southwest Jiaotong University,Chengdu 610031,China;Faculty of Geoscience and Environment Engineering,Southwest Jiaotong University,Chengdu 610031,China;Department of Civil and Environmental Engineering,Hong Kong Polytechnic University,Hong Kong 999077,China)
出处
《系统工程理论与实践》
EI
CSSCI
CSCD
北大核心
2024年第4期1376-1388,共13页
Systems Engineering-Theory & Practice
基金
国家重点研发计划项目(2022YFC3005205)
国家自然科学基金(71871189,72104205)。
关键词
疏散时间
卷积神经网络
元胞自动机
计算效率
evacuation time
convolutional neural network
cellular automaton
computational efficiency
