摘要
以地铁车站全日客流特征为基础,研究相似个体行为选择规律并差异化赋值属性参数,进而提高在突发事件下乘客应急疏散仿真结果的可靠性。首先结合车站周边用地性质和全日分时段进出站客流量划分为通勤型、商业型和枢纽型三类车站;然后用SP与RP相结合的问卷调查乘客在不同情境下疏散行为选择方式,建立基于潜类别条件的Logit模型得到乘客属性分类,并运用多元Logistics回归分析影响因素及分布概率;最后运用AnyLogic软件建立地铁车站疏散模型并对比仿真效果。结果表明,地铁站的盲从型、慌乱型、自主型和冲动型乘客比例差异对疏散效率有显著影响。
Based on the characteristics of daily passenger flow in subway stations,this paper studied the behavior selection rules of similar individuals and assigns differentiated attribute parameters,so as to improve the reliability of simulation results of passenger emergency evacuation under emergencies.Firstly,based on the nature of the surrounding land and the daily passenger flow in and out of the station,it was divided into three types:commuter,commercial,and hub stations.Then,SP and RP questionnaires were used to investigate the evacuation behavior selection modes of passengers in different situations.Logit model based on latent category conditions was established to obtain the classification of passenger attributes,and multiple Logistics regression was used to analyze the influencing factors and distribution probability.Finally,AnyLogic software was used to establish the subway station evacuation model and compare the simulation results.The results show that the proportion difference of blind,panic,independent and impulsive passengers in subway station has significant influence on evacuation efficiency.
作者
徐慧智
武笑宇
陆鹏
王连震
XU Hui-zhi;WU Xiao-yu;LU Peng;WANG Lian-zhen(School of Traffic and Transportation,Northeast Forestry University,Harbin Heilongjiang 150040,China;Harbin Metro Group Co.,Ltd.,OperationBranch Company,Harbin Heilongjiang 150050,China)
出处
《计算机仿真》
北大核心
2023年第11期111-115,138,共6页
Computer Simulation
基金
国家自然科学基金青年科学基金项目(71701041)
黑龙江省自然科学基金项目(LH2019E007)
中央高校基本科研业务费专项资金项目(2572019BG02)。
关键词
地铁客流
行人疏散
潜类别模型
多元回归
仿真
Subway passenger flow
Pedestrian evacuation
Latent category model
Multiple regression
Simulation
