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无人驾驶汽车对出行方式选择行为的影响

刘志伟 刘建荣 邓卫

刘志伟, 刘建荣, 邓卫. 无人驾驶汽车对出行方式选择行为的影响[J]. 西南交通大学学报, 2021, 56(6): 1161-1168. doi: 10.3969/j.issn.0258-2724.20200299
引用本文: 刘志伟, 刘建荣, 邓卫. 无人驾驶汽车对出行方式选择行为的影响[J]. 西南交通大学学报, 2021, 56(6): 1161-1168. doi: 10.3969/j.issn.0258-2724.20200299
LIU Zhiwei, LIU Jianrong, DENG Wei. Impact of Autonomous Vehicle on Travel Mode Choice Behavior[J]. Journal of Southwest Jiaotong University, 2021, 56(6): 1161-1168. doi: 10.3969/j.issn.0258-2724.20200299
Citation: LIU Zhiwei, LIU Jianrong, DENG Wei. Impact of Autonomous Vehicle on Travel Mode Choice Behavior[J]. Journal of Southwest Jiaotong University, 2021, 56(6): 1161-1168. doi: 10.3969/j.issn.0258-2724.20200299

无人驾驶汽车对出行方式选择行为的影响

doi: 10.3969/j.issn.0258-2724.20200299
基金项目: 湖北省自然科学基金(2020CFB290);国家自然科学基金(51578247)
详细信息
    作者简介:

    刘志伟(1987—),男,讲师,博士,研究方向为交通工程,E-mail:tonyliuzhiwei@whpu.edu.cn

    通讯作者:

    刘建荣(1984—),男,讲师,博士,研究方向为交通工程,E-mail:ctjrliu@scut.edu.cn

  • 中图分类号: U121

Impact of Autonomous Vehicle on Travel Mode Choice Behavior

  • 摘要:

    无人驾驶汽车对出行者出行选择行为具有重要影响,进而可以影响到城市交通需求、城市空间布局和城市规划. 基于扩展技术接受模型和考虑不同出行者偏好的异质性,建立带潜变量的随机系数Logit模型,研究出行者的出行特征、心理潜变量和个体的社会经济属性对无人驾驶汽车选择行为的影响. 结果表明:与传统的多项Logit模型相比,带潜变量的随机系数Logit模型拟合度更高;不同的出行者对出行费用的偏好具有异质性,在效用函数中,出行费用服从正态分布;无人驾驶汽车选择行为不仅受到出行特征和社会经济属性的影响,而且还受到感知信任、社会规范和行为意向等心理潜变量的影响;降低无人驾驶汽车的出行费用可以提升出行者选择无人驾驶汽车出行的概率.

     

  • 图 1  研究框架

    Figure 1.  Research framework

    表  1  SP情景示例

    Table  1.   Example of SP scenario

    选项步行与等待时间/min出行时
    间/min
    出行费
    用/元
    私人无人驾驶汽车 3 33 24
    共享无人驾驶汽车 8 24 29
    传统小汽车 5 29 36
    下载: 导出CSV

    表  2  样本描述性统计

    Table  2.   Descriptive statistics of samples

    变量定义表示符号百分比/%
    性别GEND45.73
    54.27
    年龄 ≤ 30 岁AGE134.47
    31~45 岁AGE228.75
    46~55 岁AGE326.85
    ≥ 56 岁AGE49.92
    受教育程度高中及以下EDU112.69
    大专EDU212.52
    本科EDU360.24
    硕士及以上EDU414.54
    职业公务员/事业
    单位人员
    OCCU 128.47
    企业员工OCCU226.62
    个体经营/自由职业OCCU321.70
    其他(兼职、学生、
    退休等)
    OCCU423.22
    家庭月收入 < 5000 元HINC124.47
    5000~10000 元HINC241.83
    10000~20000 元HINC321.93
    > 20000 元HINC411.77
    是否有学龄儿童CHILD50.55
    49.45
    是否有驾照LICENSE50.78
    49.22
    家庭是否有小汽车CAR64.92
    35.08
    是否有公交IC卡ICARD77.55
    22.45
    是否使用小汽车通勤 CAR2WORK62.60
    67.40
    家庭总人口1 人HSIZE6.92
    2 人9.46
    3 人43.22
    4 人18.47
    5 人及以上21.93
    下载: 导出CSV

    表  3  样本数据的信度及效度检验

    Table  3.   Reliability and validity test of samples

    潜变量题项KMO因子载荷Cronbach’s Alpha
    感知有用性pu10.8410.9050.917
    pu20.913
    pu30.910
    pu40.850
    感知易用性peu10.7700.9450.945
    peu20.957
    peu30.946
    感知信任pt10.7640.9600.956
    pt20.970
    pt30.947
    社会规范sn10.7620.9670.954
    sn20.963
    sn30.942
    行为意向biu10.7700.9740.964
    biu20.963
    biu30.959
    下载: 导出CSV

    表  4  个人属性对感知易用性的影响

    Table  4.   Impact of travelers’ demographic characteristics on potential variable PEU

    变量估计参数P
    性别0.3920
    年龄−0.1350
    受教育程度0.0470.004
    职业−0.1080
    家庭月收入−0.0030.832
    是否有学龄儿童0.0830
    是否有驾照0.2350
    家庭是否有小汽车0.1780
    是否有公交 IC 卡0.0250.325
    家庭总人口−0.0220.012
    下载: 导出CSV

    表  5  带潜变量的RPLM和带潜变量的MNLM参数标定结果

    Table  5.   Estimation results of the RPLM with latent variables and MNLM with latent variables

    出行
    方式
    变量带潜变量的
    RPLM
    带潜变量的MNLM
    估计
    参数
    Z 估计
    参数
    Z
    私人无人驾驶汽车 AGE1 1.271 5.87 1.201 6.00
    EDU3 0.539 1.87 0.466 1.73
    HINC4 −2.017 −8.22 −1.928 −8.49
    LICENSE 0.390 1.97 0.356 1.91
    CAR 0.166 0.74 0.182 0.84
    ICARD −0.562 −2.42 −0.575 −2.63
    CAR2WORK 0.086 0.38 0.135 0.63
    HSIZE 0.287 4.35 0.258 4.31
    PT 1.270 2.96 1.188 2.94
    SN −0.499 −2.41 −0.428 −2.25
    BIU 1.765 3.78 1.668 3.81
    共享无人驾驶汽车 AGE1 0.759 3.50 0.698 3.55
    EDU3 1.042 3.76 0.985 3.92
    HINC4 −1.439 −5.90 −1.353 −6.18
    LICENSE 0.056 0.28 0.047 0.25
    CAR 0.373 1.66 0.365 1.77
    ICARD −0.154 −0.65 −0.181 −0.84
    CAR2WORK −2.005 −7.39 −1.826 −8.02
    HSIZE 0.303 4.25 0.269 4.31
    PT 0.936 2.23 0.878 2.31
    SN −0.181 −0.91 −0.118 −0.66
    BIU 1.522 3.33 1.387 3.38
    TC 均值 −0.029 −4.77 −0.029 −5.31
    TC 标准差 0.030 2.51
    WWT −0.099 −5.59 −0.093 −5.65
    TT −0.037 −4.75 −0.033 −4.84
    下载: 导出CSV

    表  6  出行费用的边际效用

    Table  6.   Marginal effects of travel costs %

    出行费用
    增加 1%
    选择私人无人驾驶汽车概率选择共享无人驾驶汽车概率选择传统小汽车概率
    私人无人驾驶汽车−1.7871.5360.251
    共享无人驾驶汽车0.846−1.0710.225
    传统小汽车0.3460.575−0.920
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-05-26
  • 修回日期:  2020-11-14
  • 网络出版日期:  2021-09-09
  • 刊出日期:  2020-12-25

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