常态化疫情防控阶段旅客中长距离城际出行联合选择行为建模

方瑞韬; 邵海鹏; 林涛

doi:10.3963/j.jssn.1674-4861.2023.01.016

常态化疫情防控阶段旅客中长距离城际出行联合选择行为建模

doi: 10.3963/j.jssn.1674-4861.2023.01.016

长安大学运输工程学院西安 710064

基金项目:

国家重点研发计划项目 2019YFB1600302

详细信息

作者简介:
方瑞韬（1998—），硕士研究生.研究方向：出行行为. E-mail：fangruitao@chd.edu.cn

通讯作者:
邵海鹏（1978—），博士，教授.研究方向：交通设计、出行行为等. E-mail：shaohp@chd.edu.cn

中图分类号: U125
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出版历程
- 收稿日期: 2022-07-01
- 网络出版日期: 2023-05-13

A Joint Mode Choice Behavior Model of Long-distance Intercity Passenger Travel during the Periods with Regular Epidemic Prevention and Control Measures

College of Transportation Engineering, Chang'an University, Xi'an 710064, China

摘要

摘要: 新冠肺炎疫情对旅客中长距离的城际交通出行影响巨大，现有研究侧重疫情暴发初期疫情对城际交通出行的影响，针对常态化疫情防控阶段旅客城际出行选择行为的研究相对较少，因此，本文旨在研究常态化疫情防控阶段旅客中长距离城际出行选择行为。针对民航、高铁、普铁和自驾等方式分别建立包含4种城际出行方式的多指标多因果出行选择模型（MIMIC），模型中引入感知防疫安全程度、防疫策略、乘车体验与出行习惯4个潜变量，探究潜变量与观测变量的因子载荷并辨识模型参数，求取各潜变量的拟合值；在此基础上建立考虑出行方式特性、旅客社会经济属性与潜变量的多出行方式联合选择行为模型（MIMIC-Logit），探究常态化疫情防控阶段旅客出行心理对其出行决策的影响；假设出行费用、时间与距离等变量的随机系数服从正态分布，采用抽样1000次的Halton序列对随机系数进行仿真求解，得到随机系数的回归分析结果。以2021年4月—6月到达西安旅客的调查数据为例进行实证研究，结果发现：所提MIMIC-Logit模型的拟合优度与命中率分别为43.621%与83.312%，均高于多项Logit模型与随机系数Logit模型；旅客对不同方式的出行费用、时间与距离的偏好具有异质性，且出行方式特性、社会经济属性与潜变量都对出行选择的效用有显著影响。弹性分析表明，当感知防疫安全程度与防疫策略提升了100%时，旅客选择民航出行的概率分别提升了23.207%与21.349%；而当乘车体验提升了100%时，旅客选择高铁出行的概率提升了18.229%。综上，所提方法揭示了潜变量对旅客出行选择行为的显著影响；通过提升感知防疫安全程度、防疫策略与乘车体验等手段，可以提升旅客选择高铁、民航出行的概率。
- 交通工程 /
- 城际出行选择 /
- MIMIC-Logit模型 /
- 潜变量 /
- 新冠疫情
Abstract: The impact of COVID-19 on long-distance intercity travel is enormous. Existing studies have investigated the impact of COVID-19 on intercity travel at the early stage of the epidemic outbreak, while few of them have studied its impact during the periods with regular prevention and control measures. To fill the gap, this paper focuses on the mode choice behavior of long-distance intercity travel under the impact of regular prevention and control measures of the COVID-19 epidemic. First, a set of multiple indicators and multiple causes (MIMIC) models are developed for civil aviation, high-speed rail, train, and passenger car, independently, and each covers the four modes. The perceived level of safety of prevention measures, epidemic prevention strategies, riding experience, and travel habits are considered in the MIMIC choice behavior model, which are used to explore the relationship between observed and latent variables, to identify the parameters of the model, and to estimate each latent variable. Secondly, to investigate the impact of passengers' psychology on their travel mode choices, a MIMIC-Logit model considering the characteristics of travel modes, socio-economic attributes of passengers, and latent variables is developed. Then, assuming that the random coefficients of passengers' travel expenses, travel time, and travel distance follow a normal distribution, the Halton sequence drawn from the original data through 1000 samplings is used to estimate the utility coefficients of the MIMIC-Logit model. Lastly, the survey data of passengers arriving in Xi'an between April and June 2021 is employed to validate the proposed model. Study results show that (1) the goodness of fit and hit ratio of the MIMIC-Logit model with latent variables is 43.621% and 83.312%, respectively, which are higher than the comparative multinomial-Logit model and the random coefficient Logit model; (2) the preferences of passengers towards different travel modes of travel expenses, travel time, and travel distance are heterogeneous, and the characteristics of travel modes, socio-economic attributes, and latent variables all have a significant impact on mode choices; (3) when the variables representing perceived level of safety of the COVID-19 prevention measures and epidemic prevention strategies is increased by 100%, the probability of choosing civil aviation is increased by 23.207% and 21.349%, respectively; (4) when the variable representing travel experience is increased by 100%, the probability of passengers choosing high-speed rail is increased by 18.229%. In general, the proposed method reveals that the latent variables representing passenger's psychology has a significant impact on mode choice behavior, and the probability of choosing high-speed rail and civil aviation can be increased by improving the perceived level of safety of prevention measures, epidemic prevention strategies, and riding experience.
- transportation engineering /
- intercity travel choice /
- MIMIC-Logit integrated model /
- latent variables /
- COVID-19

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图 1 MIMIC-Logit模型的框架

Figure 1. The framework of MIMIC-Logit integrated model

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图 2 MIMIC模型的结构

Figure 2. The structure of the MIMIC model

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图 3 MIMIC模型求解结果

Figure 3. Results of MIMIC model

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图 4 潜变量对出行方式选择概率的弹性

Figure 4. Elasticity of latent variables to intercity travel mode choice

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表 1 潜变量与其指标观测变量的对应关系

Table 1. The relationship between latent variables and their indicators

潜变量	变量代码	指标观测变量
感知防疫安全程度	PS	我认为该方式的防疫服务水平是安全的我认为该方式能提供有效的防疫保障措施我认为乘坐该方式不增加疫情防控的难度
防疫策略	S	我认为该方式的消杀通风程度很好我认为该方式能保持旅客间的安全距离我认为该方式的防疫宣传很好
乘车体验	TE	我认为该方式的内部环境很好我认为该方式的座椅舒适我认为该方式传播病毒的概率较低我认为该方式的防疫设施完善
出行习惯	H	即使出现疫情，该方式仍然是我的最优选择疫情影响下，我更倾向于乘坐该方式疫情爆发前，我乘坐该方式的频率很高

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表 2 显变量定义

Table 2. Definition of observable variables

类别	变量名称	变量代码	变量解释
出行特征	城市等级	CL	一线城市；二线城市；三线城市；四线城市
	出行距离/km	D	出发城市与到达城市之间的直线距离
	出行费用/元	CO	城际出行总费用
	出行时长/h	T	0~2；＞2~3；＞3~4；＞4~5；＞5~6；＞6
	换乘次数	CH	无需换乘；1次换乘；超过1次换乘
	出发时刻	DT	当日13：00之前；当日13：00之后
	天气	W	晴天；多云；雨、雪、雾等
	行李数	B	有无大件行李
	同行人数/人	P	0；1；≥ 2
	出行目的	PU	旅游；其他
社会经济属性	月收入/元	I	0~3 000；＞3 000~6 000；＞6 000~9 000；＞9 000~12 000；＞12 000
	受教育程度	E	初中及以下；高中/中专；大专；本科；硕士及以上
	职业	J	学生；国有企业；事业单位；公务员；民营企业；外资企业
	年龄/岁	A	0~20；＞20~26；＞26~32；＞32~39；＞39~46；＞46~53；＞53
	性别	G	男；女
	小汽车拥有量	CA	是；否

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表 3 MIMIC模型拟合度评价指标

Table 3. Fitness evaluation index of the MIMIC model

拟合度评价指标	MIMIC-P模型	MIMIC-H模型	MIMIC-T模型	MIMIC-C模型	推荐范围
拟合度评价指标	实际值	实际值	实际值	实际值	推荐范围
CMIN/df	1.665	1.533	1.776	1.543	< 3.0
RMSEA	0.045	0.036	0.044	0.041	< 0.05
GFI	0.917	0.918	0.907	0.916	>0.90
CFI	0.975	0.976	0.957	0.976	>0.90
TLI	0.966	0.974	0.958	0.971	>0.90
注：MIMIC-P模型、MIMIC-H模型、MIMIC-T模型，以及MIMIC-C模型分别为民航、高铁、普铁以及自驾出行的MIMIC模型。

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表 4 MIMIC模型的结构模型结果

Table 4. Results of the structure model

方式	潜变量	E	J	A	G	I	CA
民航	PS	0.127			0.097
	S	0.114		0.106	0.094
	TE		0.108			0.131
	H		0.151				-0.126
高铁	PS	0.134			0.094
	S	0.131		0.120	0.108
	TE		0.137			0.114
	H		0.136				-0.154
普铁	PS	0.144			0.093
	S	0.106		0.135	0.117
	TE		0.103			0.127
	H		0.121				-0.099
自驾	PS	0.114			0.095
	S	0.154		0.098	0.108
	TE		0.132			0.115	0.138
	H		0.128				0.117
注：表中参数值均满足p < 0.1的显著性检验。

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表 5 MNL模型与MIMIC-Logit模型的回归分析结果

Table 5. Regression analysis results of MNL model and MIMIC-Logit integrated model

	变量	MNL模型			随机系数Logit模型			MIMIC-Logit模型
	变量	高铁	普铁	自驾	高铁	普铁	自驾	高铁	普铁	自驾
出行方式特征	CO	-0.079***	-0.093*	0.041***	-0.096***	-0.046*	0.037***	-0.075***	-0.067**	0.043***
	T				-0.256**	0.647*	-0.624*	-0.156*	0.732*	-0.557**
	D	-0.017*	0.033**	-0.074*	-0.035*	0.030**	-0.079*	-0.013**	0.040**	-0.061*
	DT	-0.876*		-1.403*	-0.880*		-1.432*	-0.833*		-1.275*
	W	0.705*		-0.108*	0.776*		-0.119*	0.376**		-0.274*
	B	0.064*		0.332**	0.058*		0.339**	0.096**		0.338**
	P	-0.196*		0.048**	-0.198*		0.042**	-0.142**		0.083*
社会经济属性	CA	-0.224**	-0.330**	1.486***	-0.236**	-0.358**	1.320***	-0.245**	-0.330**	1.375***
	E	-0.427**		-0.641**	-0.441**		-0.627**	-0.461**		-0.602**
	A	0.457**	0.159*	-0.267**	0.466**	0.143*	-0.257**	0.448**	0.165*	-0.264**
	G	0.132**	0.170**	-0.162**	0.158**	0.186**	-0.164**	0.180**	0.196**	-0.142**
	I	-0.664*	-0.830**	-0.770**	-0.667*	-0.833**	-0.756**	-0.643*	-0.827**	-0.763**
潜变量	PS							-0.658**	-0.440**	-1.405***
	S							-0.426*	-0.236**	-0.649*
	TE							-0.214*	0.089*	-0.423**
	H							-0.246*	1.442**	1.907*
常数项	CON	-2.332**	-1.661*	-3.049**	-2.368**	-1.627*	-3.421**	-1.033**	0.356**	-1.744***
对数似然估计值			-808.232			-787.306			-729.513
拟合优度比/%			34.713			36.422			43.621
命中率/%			77.89			79.83			83.31
注：，，**表示显著性水平为1%，5%，10%；随机系数Logit模型与MIMIC-Logit模型中，CO，T，D的参数结果为其服从正态分布的均值。

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表 6 需求弹性估计值

Table 6. Demand elasticity estimates

出行方式	CO	D	PS	S	TE
民航	0.140	-0.124	-0.233	-0.208	-0.048
高铁	-0.123	-0.115	-0.091	-0.139	-0.176
普铁	-0.158	-0.076	-0.154	-0.057	-0.033
自驾	0.059	-0.118	-0.172	-0.174	-0.080

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