A Quantitative Method for Studying Passenger Decision-Making Preference in Subway Emergency Evacuation

doi:10.11908/j.issn.0253-374x.21225

Home > Archive>Volume 50, Issue 4, 2022 >571-579. DOI:10.11908/j.issn.0253-374x.21225

A Quantitative Method for Studying Passenger Decision-Making Preference in Subway Emergency Evacuation
DOI:
                        10.11908/j.issn.0253-374x.21225
                    
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                        WANG Heng1WANG Heng
Key Laboratory of Road and Traffic Engineering of the Ministry of Education， Tongji University， Shanghai 201804， China
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LI Feng1LI Feng
Key Laboratory of Road and Traffic Engineering of the Ministry of Education， Tongji University， Shanghai 201804， China
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JIANG Zehao2JIANG Zehao
School of Civil and Hydraulic Engineering， Huazhong University of Science and Technology， Wuhan 430074， China
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XU Tiandong3,4XU Tiandong
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
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Affiliation:1.Key Laboratory of Road and Traffic Engineering of the Ministry of Education， Tongji University， Shanghai 201804， China;2.School of Civil and Hydraulic Engineering， Huazhong University of Science and Technology， Wuhan 430074， China;3.Urban Transport Institute， China Academy of Urban Planning and Design， Beijing 100044， China;4.College of Design， Construction and Planning， University of Florida， Gainesville 32611-5706， USA
Clc Number:U293.1
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Abstract:

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.

Key words:urban subway stations;emergency evacuation behavior;random parameters Logit model;preference heterogeneity

Reference

[1] 中国城市轨道交通协会. 城市轨道交通2018年度统计和分析报告[J].城市轨道交通，2019（4）：16.

[2] 吴君子. 火灾条件下地铁疏散仿真研究[D]. 成都：西南交通大学， 2016.

[3] 蓝善民. 高铁站应急疏散研究[D]. 成都：西南交通大学， 2014.

[4] 丁玉兰. 人机工程学[M] . 4版.北京：北京理工大学出版社， 2011.

[5] XU Tiandong. Dynamic travel behavior and active traffic routing and control modeling for regional evacuations[M].[s.l.]：Independent Press，2015.

[6] XU T D， HAO Y， SUN L J. Modeling drivers’ en-route diversion behavior under variable message sign messages using real detected traffic data[J].IET Intelligent Transport Systems， 2011， 5（4）： 294.

[7] HELBING Dirk， BUZNA Lubos， JOHANSSON Anders， et al. Self-organized pedestrian crowd dynamics： Experiments， simulations， and design solutions[J]. Transportation Science， 2013， 219（5）：5495.

[8] BLUE V， ADLER J. Emergent fundamental pedestrian flows from cellular automata microsimulation[J]. Transportation Research Record， 1998 （1644）：29.

[9] ZHONG M， SHI C， TU X， et al. Study of the human evacuation simulation of metro fire safety analysis in China[J]. Journal of Loss Prevention in the Process Industries， 2008， 21（3）：287.

[10] GR??LE F， KRETZ T. An example of complex pedestrian route choice[J].Pedestrian and Evacuation Dynamics，2011（6）：767.

[11] JIANG C S， YUAN F， CHOW W K. Effect of varying two key parameters in simulating evacuation for subway stations in China[J]. Safety Science， 2010， 48（4）：445.

[12] 吴鹏，杨爽，张晶晶，等. 突发事件网络舆情中网民群体行为演化的Agent建模与仿真研究[J]. 现代图书情报技术， 2015（Z1）：65.

[13] XU Tiandong. Big-data-driven ATMS： Driver behavior and active traffic management[M].[s.l.]： Independent Press，2017.

[14] XU T D， HAO Y， SUN L J AND . Automatic calibration of behavioral parameters for variable message signs-based route guidance consistent with driver behavior[J].Transportation Research Record： Journal of the Transportation Research Board， 2012， 2321（2321）： 55.

[15] ANTONINI G， BIERLAIRE M， WEBER M. Discrete choice models of pedestrian walking behavior[J]. Transportation Research Part B， 2006， 40（8）：667.

[16] LOVREGLIO R， BORRI D，Dell OLIO L， et al. A discrete choice model based on random utilities for exit choice in emergency evacuations[J]. Safety Science， 2014， 62：418.

[17] DUIVES D， MAHMASSANI H. Exit choice decisions during pedestrian evacuations of buildings[J]. Transportation Research Record： Journal of the Transportation Research Board， 2012， 2316：84 .

[18] 王方，陈金川，张德欣. SP调查在交通方式选择模型中的应用[J]. 交通运输系统工程与信息， 2007（5）：90.

[19] WILSON T. Monte Carlo analysis of SP-off-RP data[J]. Journal of Choice Modelling， 2009（2）：101.

[20] HAGHANI M， SARVI M. Pedestrian crowd tactical‐level decision making during emergency evacuations[J]. Journal of Advanced Transportation， 2016， 50（8）：187.

[21] ANWAR A H M M， TIEU K， GIBSON P， et al. Analyzing the heterogeneity of traveller mode choice preference using a random parameter logit model from the perspective of principal-agent theory[J]. International Journal of Logistics Systems & Management， 2014， 17（4）：447.

[22] ROSE J M， HESS S. Dual-response choices in pivoted stated choice experiments[J]. Transportation Research Record： Journal of the Transportation Research Board， 2009， 2135（1）：25.

[23] TRAIN K， WILSON W W. Estimation on stated-preference experiments constructed from revealed-preference choices[J]. Transportation Research Part B： Methodological， 2008， 42 （3）：191.

[24] 高永祥，张晋昕.回归分析的样本量确定[J]. 循证医学， 2018， 18（2）：3.

[25] 方积乾. 卫生统计学[M] .6版. 北京：人民卫生出版社， 2008.

[26] 金丕焕. 医用统计方法[M]. 上海：复旦大学出版社， 2003.

[27] PEDUZZI P， CONCATO J， KEMPER E， et al. A simulation study of the number of events per variable in logistic regression analysis[J]. Journal of Clinical Epidemiology， 1996， 49 （12）：1373.

[28] TRAIN M F. Mixed MNL models for discrete response[J]. Journal of Applied Econometrics， 2000， 15（5）：447.

[29] 刘建荣，郝小妮. 基于随机系数Logit模型的市内出行方式选择行为研究[J]. 交通运输系统工程与信息， 2019（5）：108.

[30] HENSHER D A， ROSE J M， GREENE W H. Nlogit for applied choice analysis[M]// DAVID Hensher. Applied Choice Analysis. London：Cambridge University Press，2015.

[31] CAO Baogui. A new car-following model considering driver's sensory memory[J]. Physica A Statistical Mechanics & Its Applications，2015， 427：218.

[32] 江泽浩，杨晓光，汪涛. 绿灯倒计时影响下机动车微观驾驶行为分析与决策建模[J].交通运输系统工程与信息， 2018， 18（2）：66.

[33] 刘建荣，郝小妮. 基于随机系数Logit模型的地铁拥挤度影响参数研究[J]. 华南理工大学学报（自然科学版）， 2019， 391（4）：67.

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WANG Heng, LI Feng, JIANG Zehao, XU Tiandong. A Quantitative Method for Studying Passenger Decision-Making Preference in Subway Emergency Evacuation[J].同济大学学报(自然科学版),2022,50(4):571~579

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Received:May 23,2021
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Online: May 06,2022
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