摘要
为提高地铁站乘客疏散时的安全指数,有必要对瓶颈区域楼梯处的乘客疏散时间进行预测以评估其通行安全性。首先,针对地铁站乘客沿楼梯上行疏散数据难以采集的问题,采用MassMotion仿真软件搭建楼梯场景,模拟乘客沿楼梯上行疏散行为,获取疏散时间基础数据;然后,利用基础数据训练和测试随机森林模型,实现乘客沿楼梯上行疏散时间的预测;最后,以疏散时间、乘客密度、疏散恐慌度为指标,建立疏散安全综合评估模型,用于评估地铁站乘客沿楼梯上行的疏散安全等级。结果表明:所采用的随机森林模型预测结果的平均绝对误差(MAE)为3.45 s,平均绝对百分比误差(MAPE)为3.8%,相较于反向传播神经网络(BPNN)模型和支持向量回归(SVR)模型具有更高的预测准确度;采用疏散安全综合评估模型评估青岛某地铁站的楼梯安全性,得到早高峰时期的疏散安全性评估值为中等。
Stairs are the bottleneck areas in the process of passenger evacuation in the subway station.The safety assessment of passengers passing through the stairs helps to formulate the evacuation plan in advance.Firstly,aiming at the difficulty of collecting the evacuation data of passengers ascending the stairs,MassMotion simulation software was adopted to build a stair scene to simulate the evacuation behavior of passengers ascending the stairs,and the basic data of evacuation time were obtained.Then,the random forest model was trained and tested with basic data to predict the evacuation time of passengers ascending the stairs.Finally,a comprehensive evaluation model of evacuation safety was established,and the evacuation safety level of passengers ascending the stairs in the subway station was evaluated with evacuation time,passenger density and evacuation panic as indicators.The research results indicate that mean absolute error(MAE)of the prediction results of the random forest model used in this paper is 3.45 s,and mean absolute percentage error(MAPE)is 3.8%.Compared with back propagation neural network(BPNN)model and support vector regression(SVR)model,the prediction accuracy is higher.The comprehensive evaluation model of evacuation safety is used to evaluate the safety of the stairs in a subway station in Qingdao,and the evaluation value of evacuation safety in the early peak period is medium.
作者
杨晓霞
蒋海龙
李永行
潘福全
杨金顺
YANG Xiaoxia;JIANG Hailong;LI Yongxing;PAN Fuquan;YANG Jinshun(School of Information and Control Engineering,Qingdao University of Technology,Qingdao Shandong 266520,China;School of Civil Engineering,Qingdao University of Technology,Qingdao Shandong 266520,China;Beijing Key Laboratory of Traffic Engineering,Beijing University of Technology,Beijing 100124,China)
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2023年第5期168-173,共6页
China Safety Science Journal
基金
国家自然科学基金资助(62003182)。
