公共卫生事件场景下轨道交通限流时乘客出行方式选择行为

窦雪萍; 史璐; 董冉; 李同飞

doi:10.3963/j.jssn.1674-4861.2024.06.018

公共卫生事件场景下轨道交通限流时乘客出行方式选择行为

doi: 10.3963/j.jssn.1674-4861.2024.06.018

北京工业大学交通工程北京市重点实验室北京 100124

基金项目:

国家自然科学基金项目 52002008

北京市自然科学基金项目 9242002

北京市社会科学基金项目 22GLC060

详细信息

作者简介:
窦雪萍（1988—），博士，副教授. 研究方向：公共交通系统建模与优化. E-mail: douxp@bjut.edu.cn

通讯作者:
李同飞（1990—），博士，副教授. 研究方向：复杂交通系统建模与分析. E-mail: tfli@bjut.edu.cn

中图分类号: U491.1
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出版历程
- 收稿日期: 2023-11-23
- 网络出版日期: 2025-03-08

Travel Mode Choice of Passengers Under Passenger Flow Control of Rail Transit During Public Health Events

Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China

摘要

摘要: 为减少重大公共卫生事件场景下城市轨道交通限流运行带来的负面影响，采用短途公交将限流车站站外候车乘客运送至周边未限流车站。分析了短途公交引入后乘客出行方式选择行为，探究利用短途公交疏解轨道交通限流车站客流的可行性。通过线上调查北京、上海、广州、深圳城市居民共回收989份有效问卷；基于问卷调查数据构建重大公共卫生事件下轨道交通限流时乘客出行方式选择Logit模型，研究此情境下影响乘客选择轨道交通单一出行方式和短途公交与轨道交通组合出行方式的显著因素及其影响作用；探索在不同的疫情感知程度下，乘客偏好对组合出行方式选择概率的调节作用；对比分析不同城市、不同类别乘客出行方式选择行为影响因素。结果表明：出行准时性偏好、出行舒适性偏好与疫情感知程度对乘客选择组合出行方式产生正向显著影响；其中，出行准时性偏好对北京、上海、广州、深圳居民选择组合出行方式均产生积极影响，对长距离直达乘客选择组合出行方式的正向影响最为显著。多因素交叉影响分析中发现，疫情感知程度为2级及以上时，对出行准时性、出行舒适性存在显著偏好的乘客选择组合出行方式的概率超过57%和53%。这些结论表明重大公共卫生事件下准时舒适的短途公交服务可以有效均衡轨道交通站点间客流负载。
- 城市交通 /
- 公共交通 /
- 出行方式选择 /
- Logit模型 /
- 限流
Abstract: To lessen the negative effects of passenger flow control during major public health events, it is suggested to employ the short-distance buses to transport waiting passengers from the restricted-flow station to the nearby stations that are operating normally. Travel mode choice of passengers toward the short-distance buses is modeled to analyze the feasibility of utilizing short-distance buses to reduce congestions at flow-restricted metro stations. A total of 989 valid questionnaires are obtained using an online survey conducted among residents in Beijing, Shanghai, Guangzhou, and Shenzhen, based on which a Logit model is established to analyze passengers' mode choices during the period of passenger flow control under major public health events. The model explores the determinants of passengers' choice between rail transit as a singular mode and the combined mode of short-distance buses and metro. Furthermore, the effects of passengers' preferences on the probability of choosing the combined travel mode under different levels of epidemic perceptions are also studied. The factors influencing the choices of travel modes of passengers among different cities and groups are compared. The results indicate that punctuality preference, comfort preference, and level of epidemic perception have a positive and significant effect on passengers' choice of the combined travel mode. Specifically, the preference toward punctuality has a significantly positive effect on the travel mode choices of the residents in all the four cities, and it has the largest effect on passengers of long-distance and direct travels. Furthermore, the results of cross-impact analysis indicate that when the epidemic perception is at Level 2 or above, the probability of passengers with a significant preference toward punctuality and comfort choosing the combined travel mode is more than 57% and 53%, respectively. The findings imply that punctual and comfortable short-distance buses can effectively equilibrate passenger distribution among metro stations during major public health events.
- urban transport /
- public transport /
- travel mode choice /
- Logit model /
- passenger flow control

HTML全文

图 1 出行方式选择情境示意图

Figure 1. Illustration of travel mode choice scenarios

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图 2 受访者所在城市

Figure 2. Cities of respondents

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图 3 心理因素调查结果

Figure 3. Psychological factors survey results

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图 4 组合出行方式选择概率变化

Figure 4. Changes in the probability of choosing a combined travel mode

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图 5 不同通勤时长乘客出行方式选择模型

Figure 5. Travel mode choice models for passengers with various commuting times

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图 6 不同换乘次数乘客出行方式选择模型

Figure 6. Travel mode choice models for passengers with various transfer times

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表 1 验证性因子分析结果

Table 1. Confirmatory factor analysis results

指标	判断标准值^[15]	模型值
近似误差均方根	≤0.080	0.062
拟合优度指数	≥0.900	0.955
非规范拟合系数	> 0.9	0.911

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表 2 个人属性、出行特征与公交服务接受程度

Table 2. Personal attributes, travel characteristics, and passenger acceptance of bus services

属性类别	特征变量	变量定义（样本百分比/%）
个人属性	A₁	男（40.44）、女（59.56）
	A₂	≤20岁（3.44）、＞20~30岁（47.12）、＞30~40岁（38.42）、＞40~50岁（8.7）、＞50岁（2.32）
	A₃	高中及以下（5.46）、大专（12.34）、本科（71.99）、硕士及以上（10.21）
出行特征	B₁	≤30 min（31.55）、＞30~60 min（54.7）、＞60~90 min（12.54）、＞90 min（1.21）
	B₂	0天（12.74）、1天（7.89）、2天（14.16）、3天（9.61）、4天（6.76）、≥5天（48.84）
	B₃	0次（25.38）、1次（37.92）、2次（26.49）、3次（6.77）、≥4次（3.44）
	B₄	≤3.0元（24.87）、＞3.0~6.0元（51.16）、＞6.0~9.0元（19.82）、＞9.0元（4.15）
公交服务接受程度	C₁	10 min（32.05）、15 min（36.20）、20 min（21.33）、≥25 min（10.41）
	C₂	5 min（30.94）、10 min（38.42）、15 min（23.46）、≥20 min（7.18）
	C₃	无需“一人一座”（77.15）、需“一人一座”（22.85）
	C₄	免费（16.38）、50%常规公交票价（23.86）、75%常规公交票价（15.98）、常规公交票价（43.78）

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表 3 Logit模型参数

Table 3. Parameters of the Logit model

量	系数	标准差	统计量	自由度	显著性水平	优势比
D₃	-0.668	0.095	49.802	1	< 0.001	0.513
D₂	0.633	0.109	33.732	1	< 0.001	1.883
D₅	0.330	0.094	12.307	1	< 0.001	1.391
D₁	-0.329	0.113	8.384	1	0.004	0.720
C₂	-0.280	0.080	12.206	1	< 0.001	0.756
D₄	0.187	0.096	3.382	1	0.050	1.206
常量	0.520	0.617	0.709	1	0.400	1.682

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表 4 不同城市类别下Logit模型参数

Table 4. Parameters of Logit models built for different cities

城市	变量	系数	优势比
北京	C₃	0.926	2.525
	D₂	0.721	2.057
	D₁	-0.418	0.658
	常量	-0.289	0.749
上海	D₃	-0.825	0.438
	D₂	0.587	1.799
	D₄	0.467	1.595
	C₂	-0.364	0.695
	常量	0.372	1.450
广州	D₂	0.739	2.094
	D₃	-0.712	0.491
	C₂	-0.329	0.720
	常量	1.190	3.286
深圳	D₃	-0.911	0.402
	D₂	0.732	2.078
	常量	1.329	3.776

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