An Analysis of Mode Choice Behavior of Inter-city Travel in Urban Agglomeration Areas Using a Random-parameter Logit Model
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摘要: 以粤港澳大湾区城市群的广深城际运输通道为例，分析城际运输通道中影响旅客出行方式选择行为的因素及其影响。传统的多项式Logi（tMNL）模型具有无关方案独立性，无法对不同出行者的选择偏好差异进行定量分析，故应用随机系数Logit模型分析城际交通出行选择行为。选取城际出行旅客的个人社会经济属性、心理潜变量（对交通方式舒适性、可靠性和便捷性的心理感受）、城际出行方式特征变量设计问卷。采用线上与线下相结合的方式开展问卷调查，共收集534份问卷，基于此建立并求解随机系数Logit模型。随机系数Logit模型估计结果的伪R2为0.178，表明模型具有良好的拟合度。研究结果表明：城际出行旅客的收入、职业、私家车保有情况、家庭儿童数量以及对出行方式便捷性的感知对其选择行为有显著影响；而出行方式的舒适性、可靠性对城际出行方式的选择行为影响不显著；改善交通方式的便捷性对提升城际出行方式的吸引力起关键作用。因此，在城际交通规划设计、运营管理中应着重考虑便捷性对城际交通方式选择带来的影响。Abstract: In order to study the impact factors of mode choices of travel modes in inter-city transport, the Guangzhou-Shenzhen inter-city transport corridor in the Great Bay Area of Guangdong-Hong Kong-Macao in China is taken as a case study. Given that traditional multinomial Logit models have the issue of independence of irrelevant alternatives (IIA) and they cannot be used to analyze heterogeneous preferences of travelers, a random-parameter Logit model is applied in this study. An online/offline survey is carried out and a total of 534 questionnaires are obtained. In the survey, information including socio-economic attributes of travelers, psychological latent variables (i. e., perceptions toward comfort, reliability, and convenience of travel modes), and attributes of modes for inter-city travel is collected, with which a random-parameter Logit model is developed and estimated. The pseudo R2 of the model is 0.178 at convergence, indicating a good model fit. The estimation results show that the income, occupation, car ownership, number of children in a family, and the perception toward the convenience of travel modes have a significant impact on the mode choice behavior of inter-city travel in urban agglomeration areas, while comfort and reliability of inter-city travel modes are not. Meanwhile, improving the convenience of inter-city travel modes plays a key role in improving their attractiveness. In this sense, special attention should be given to the convenience of the inter-city travel modes in the process of inter-city transport planning and management.
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表 1 城际出行旅客个人属性
Table 1. Personal attributes of intercity travelers
个人属性 符号 年龄 dage 学历 dschool 月收入 dincome 性别 dfemale 职业 djob 家庭是否有私家车 dcar 是否有小孩 dchild 
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表 2 表征心理潜变量的显示变量
Table 2. Manifest variable represents latent variable
潜变量 显示变量 符号 舒适性
Lcomfort我很难忍受拥挤嘈杂的环境 c1 我很看重交通方式的舒适性 c2 我习惯乘车办公、看书或者娱乐 c3 我遇到交通拥堵、临时停车时会烦躁 c4 频繁的加减速使我感到不适 c5 可靠性
Lreliability我很看重交通方式的准点率 r1 为了更准时到达目的地，我愿意选择舒适性低的交通方式 r2 我不喜欢乘车时发生延误 r3 我出行时会预留充足的时间以防延误 r4 便捷性
Lconvenience我不会为了节省费用而多换乘 v1 我更愿意选择发班频率高的交通方式 v2 我更倾向于上下车在市区的交通方式 v3 我更喜欢乘车流程简单的交通方式 v4
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表 3 情景选择示例
Table 3. An example of SP choice scenario
问卷编号 交通方式 接驳时间/min 乘车时间/min 费用/元 请选择交通方式 A轨道交通 30 53 60 □A 问卷1 B客运班车 20 74 30 □B C私家车 5 60 67 □C A轨道交通 30 80 80 □A 问卷2 B客运班车 20 110 45 □B C私家车 5 90 100 □C A轨道交通 20 80 60 □A 问卷3 B客运班车 13 110 30 □B C私家车 3 90 67 □C A轨道交通 20 53 80 □A 问卷4 B客运班车 13 74 45 □B C私家车 3 60 100 □C
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表 4 个人属性描述性统计
Table 4. Descriptive statistics of personal attributes
个人属性 类别 比例/% ≤25 42.1 年龄/岁 > 25~50 49.5 > 50 8.4 高中及以下 26.3 学历 大专、本科 57.9 硕士及以上 15.9 ≤5 000 60.3 月收入/元 > 5 000~10 000 24.1 > 10 000 15.6 性别 男 54.8 女 45.2 职业 学生 37.2 公务员/事业单位 13.6 企业 23.1 个体户/自由职业/农民/无业 26.1 家庭是否有私家车 有 41.4 无 58.6 是否有小孩 有 42.5 无 57.5
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表 5 模型检验指标结果
Table 5. Fitness statistics of the confirmatory factor analysis
检验指标 RMSEA CFI TLI SRMR 标准 ≤0.08 ≥0.900 ≥0.900 < 0.08 模型参数 0.076 0.92 0.907 0.055
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表 6 验证性因子分析模型结果
Table 6. Results of confirmatory factor analysis
潜在变量 显示变量 标准系数 Z值 p值 Lcomfort c1 0.843 12.778 < 0.001 c2 0.633 13.188 < 0.001 c3 1.157 13.987 < 0.001 c4 0.593 12.053 < 0.001 c5 0.561 11.407 < 0.001 Lreliability r1 0.510 10.924 < 0.001 r2 1.005 14.670 < 0.001 r3 0.270 7.567 < 0.001 r4 0.754 13.933 < 0.001 Lconvenience v1 1.317 15.310 < 0.001 v2 0.420 12.668 < 0.001 v3 0.248 9.875 < 0.001 v4 0.187 8.481 < 0.001
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表 7 不考虑个人属性影响的随机系数Logit回归结果
Table 7. Results of the random-parameter Logit model without demographic characteristics
参数 系数 标准差 z值 p值 非随机项 dbus 0.176 9** 0.065 0.27 0.007 8 drailway 0.467 7** 0.060 0.78 0.004 4 P -0.100 0** 0.003 -3.10 0.000 2 β C -0.007 2 0.003 -1.55 0.120 0 T -0.175 8** 0.005 -3.48 0.000 2 σ Nc 0.000 1 0.015 0.00 0.271 1 NT 0.051 3** 0.012 5.29 0.000 1 注：dbus与drailway表示所选取的城际出行方式是否为客运班车、轨道交通的虚拟变量；Nc与NT表示接驳时间（C）、行程时间（T）的系数的标准差；“**”表示系数在5%的显著性水平下显著。
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表 8 考虑个人属性影响的随机系数Logit回归结果
Table 8. Results of the random-parameter Logit model with demographic characteristics
参数 系数 标准差 z值 p值 T:dage -0.003 0.007 -0.01 0.369 9 T:dschool 0.002 0.088 0.03 0.988 0 T:dincome 0.127** 0.607 2.09 0.003 7 T:dfemale 0.008 0.134 0.62 0.530 2 T:djob -0.051** 0.060 -0.80 0.004 2 T:dcar -0.071** 0.138 -0.52 0.000 6 T:dchild 0.081** 0.142 0.58 0.000 6 T:Lcomfort 0.013 0.014 0.92 0.700 0 T:Lreliability 0.008 0.020 0.38 0.906 2 T:Lconvenience 0.468** 0.031 1.66 0.000 4 注：T：dage表示dage对T的系数的影响，对应式（8）δ3中的1个元素，其余同理。
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表 9 考虑显著的个人属性影响的随机系数Logit回归结果
Table 9. Results of the random-parameter Logit model with significant demographic characteristics
参数 系数 标准差 z值 p值 非随机项 dbus 0.177 2** 0.646 0.27 0.007 1 drailway 0.470 2** 0.601 0.79 0.004 3 P -0.100 3** 0.003 -3.11 0.000 2 C -0.007 1 0.003 -1.58 0.124 4 β T -0.175 7** 0.005 -3.53 0.000 2 T:dincome 0.132** 0.611 2.03 0.004 0 T:djob -0.053** 0.060 -0.80 0.003 9 δ3 T:dcar -0.070** 0.158 -0.52 0.000 6 T:dchild 0.078** 0.140 0.58 0.000 5 T:Lconvenience 0.468** 0.031 1.78 0.000 4 σ NT 0.049 9** 0.012 5.13 0.000 2
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表 10 多项Logit模型回归结果
Table 10. Results of multinomial logit model
参数 系数 标准差 z值 p值 dbus 0.157 0** 0.534 0.51 0.006 0 drailway 0.455 1** 0.575 0.88 0.003 2 P -0.114 3** 0.004 -3.26 0.000 2 C -0.008 2 0.003 -1.58 0.105 6 T -0.187 3** 0.005 -3.66 0.000 2
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