摘要
为了研究距离、出口密度、乘客流向流量、出口可见性4种因素对地铁站紧急疏散时乘客决策偏好异质性的量化影响,基于20种紧急疏散场景的问卷调查收集数据,利用条件Logit模型和随机参数Logit模型标定4种影响因素的效用系数,根据效用系数的边际概率分布分析乘客决策偏好的异质性。结果表明:距离、出口密度、乘客流向流量呈负效用,出口可见性呈正效用,4种影响因素的系数均为随机变量,距离显示出最低的异质性水平,出口密度和乘客流向流量的异质性水平稍高,出口可见性显示出最高的异质性水平。
This paper aimed to study the influence of distance,density,pedestrian flow,and visibility on the heterogeneity of passenger decision-making preference in emergency evacuation in subway stations.The conditional Logit model and random parameter Logit model were used to quantify the utility coefficients of the four influencing factors based on the data collected from 20 emergency evacuation scenarios,and the heterogeneity of pedestrian decision preference was quantitatively analyzed according to the marginal probability distribution of the utility coefficients.The results show that the distance,density,and pedestrian flow have negative influences while visibility has a positive utility.The goodness of fit of the random parameter Logit model is higher than that of the conditional Logit model.The coefficients of the four influencing factors are random variables.Distance has the lowest level of heterogeneity while density and pedestrian flow have a slightly higher level of heterogeneity,and visibility has the highest level of heterogeneity.
作者
王恒
李枫
江泽浩
徐天东
WANG Heng;LI Feng;JIANG Zehao;XU Tiandong(Key Laboratory of Road and Traffic Engineering of the Ministry of Education,Tongji University,Shanghai 201804,China;School of Civil and Hydraulic Engineering,Huazhong University of Science and Technology,Wuhan 430074,China;Urban Transport Institute,China Academy of Urban Planning and Design,Beijing 100044,China;College of Design,Construction and Planning,University of Florida,Gainesville 32611-5706,USA)
出处
《同济大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2022年第4期571-579,共9页
Journal of Tongji University:Natural Science
基金
国家自然科学基金面上项目(71671109)
国家重点研发计划(2020YFB1600500)。
关键词
城市地铁站
紧急疏散行为
随机参数Logit模型
偏好异质性
urban subway stations
emergency evacuation behavior
random parameters Logit model
preference heterogeneity