Impacts of Autonomous Vehicles on Mode Choice Behavior in the Context of Short- and Medium- Distance Intercity Travel
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摘要: 研究了无人驾驶汽车对中短距离市际间出行选择行为的影响。基于计划行为理论，通过建立结构方程模型，构建出行者对无人驾驶汽车的感知行为控制、主观规范、行为态度和行为意向心理潜变量。然后将这些心理潜变量纳入到随机系数Logit模型建立混合选择模型。以武汉市为例进行实证研究，结果表明：在效用函数中，车内时间、出入站和候车时间，以及出行费用这3个变量的系数不是固定值，而是分别服从均值为-0.014，-0.008，-0.010，标准差为0.014，0.021，0.017的正态分布。个体对无人驾驶汽车的感知行为控制和行为态度每提高1个单位，采用无人驾驶汽车出行的概率分别增加64.3%和77.9%。无人驾驶汽车的出行费用和车内时间每下降1%，选择无人驾驶汽车的概率上升0.403%和0.467%。结果证实出行者对车内时间、出入站和候车时间和出行费用的偏好存在异质性，感知行为控制和行为态度对出行者选择无人驾驶汽车出行具有显著正影响，减少无人驾驶汽车的出行费用和出行时间可以提高该方式的吸引力。
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关键词:
- 交通工程 /
- 出行方式选择 /
- 计划行为理论 /
- 随机系数Logit模型 /
- 无人驾驶汽车
Abstract: This paper studies the impacts of autonomous vehicles on mode choice behavior in the context of shortand medium-distance intercity travel. Based on the theory of planned behavior, a structure equation model isdeveloped, through which latent psychological variables of individuals towards autonomous vehicles are developed, including perceived behavioral control, subjective norms, attitudes, and behavioral intentions. These latent psychological variables are then integrated into a random parameter Logit model to develop a hybrid choice model. The City of Wuhan is used as a case to carry out an empirical study, and the study results show that: in the utility function, the coefficients of three variables, including in-vehicle time, access and exit and waiting time, and travel cost, are not fixed but follow a normal distribution with a mean of -0.014, -0.008, and -0.010 and with the standard deviations of 0.014, 0.021, and 0.017, respectively. When the perceived behavior control and attitude of individuals towards autonomous vehicles increased by 1 unit, the probability of using autonomous vehicles to travel increased by 64.3% and 77.9%, respectively. For every 1% decrease in the travel cost and in-vehicle time of autonomous vehicles, the probability of choosing autonomous vehicles and intercity shuttles increases by 0.403% and 0.467%, respectively. This paper studies the impacts of autonomous vehicles on mode choice behavior in the context of shortand medium-distance intercity travel. Based on the theory of planned behavior, a structure equation model isdeveloped, through which latent psychological variables of individuals towards autonomous vehicles are developed, including perceived behavioral control, subjective norms, attitudes, and behavioral intentions. These latent psychological variables are then integrated into a random parameter Logit model to develop a hybrid choice model. The City of Wuhan is used as a case to carry out an empirical study, and the study results show that: in the utility function, the coefficients of three variables, including in-vehicle time, access and exit and waiting time, and travel cost, are not fixed but follow a normal distribution with a mean of -0.014, -0.008, and -0.010 and with the standard deviations of 0.014, 0.021, and 0.017, respectively. When the perceived behavior control and attitude of individuals towards autonomous vehicles increased by 1 unit, the probability of using autonomous vehicles to travel increased by 64.3% and 77.9%, respectively. For every 1% decrease in the travel cost and in-vehicle time of autonomous vehicles, the probability of choosing autonomous vehicles and intercity shuttles increases by 0.403% and 0.467%, respectively. Study results show that travelers have heterogeneous preferences toward the attributes of the transport service offered by autonomous vehicles, such as in-vehicle time, access/egress and waiting time, and travel costs. It is also found that perceived behavioral control and behavioral attitudes have significantly positive impacts on traveler's choice on autonomous vehicles. Therefore, reducing travel costs and travel time of autonomous vehicles can increase the attractiveness of autonomous vehicles. -
表 1 表征心理潜在变量的显示变量
Table 1. Indicator Variables of psychological latent variables
潜在变量 显示变量 符号 感知行为控制 是否使用无人驾驶车辆完全取决于我自己 pbc1 我可以负担得起使用无人驾驶车辆出行 pbc2 我有充足的机会使用无人驾驶车辆出行 pbc3 主观规范 对我很重要的人支持我使用无人驾驶车辆 sn1 对我很重要的人希望我将来能使用无人驾驶车辆 sn2 如果身边的人使用无人驾驶车辆，我也会使用 sn3 行为态度 使用无人驾驶车辆是令人愉快的 att1 使用无人驾驶车辆是积极的 att 2 使用无人驾驶车辆是值得向往的 att 3 行为意向 未来我会使用无人驾驶车辆 biu1 未来我会购买无人驾驶车辆 biu2 我会向亲朋好友推荐使用无人驾驶车辆 biu3 
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表 2 出行方式的变量及变量水平
Table 2. Attributes and attributes levels of each mode of transport
出行方式 车内时间/min 出入站时间/min 候车时间/min 出行费用/元 火车 0.7|0.9|1.2×高铁运行时间 20|40|60 20|40|60 0.7|0.9|1.2×高铁出行费用 无人驾驶汽车 0.7|0.9|1.2×车内时间 0.7|0.9|1.2×出行费用 市际班车 0.7|0.9|1.2×班车运行时间 20|40|60 20|40|60 0.7|0.9|1.2×班车出行费用
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表 3 样本描述性统计
Table 3. Descriptive statistics of sample
变量 表示符号 定义 百分比/% 性别 GEND 女 48.42 男 51.58 ≤30 42.46 年龄/岁 AGE >30~45 32.63 >45~55 13.68 >55 11.23 高中及以下 9.83 受教育程度 EDU 大专 16.84 本科 43.51 硕士及以上 29.82 公务员\事业单位人员 28.07 职业 OCCU 企业员工 31.93 个体经营\自由职业 12.98 其他 27.02 ≤5 000 19.30 家庭月收入/元 HINC >5 000~10 000 39.30 >10 000~20 000 25.61 >20 000 15.79 是否有学龄儿童 CHILD 是 40.70 否 59.30 是否拥有驾照 LICEN 是 70.53 否 29.47 家庭是否拥有小汽车 CAR 是 72.28 否 27.72 是否拥有公交IC卡 ICARD 是 78.25 否 21.75 1 8.42 家庭总人口数 HSIZE 2 19.65 3 39.30 4人及以上 32.63 1 26.84 同行者人数 PASSEN 2 28.60 3 32.45 4 12.11
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表 4 样本数据的信度和效度检验
Table 4. Reliability and validity test of sample
变量 题项 Cronbach's α 因子载荷 KMO pbc1 0.941 感知行为控制 pbc2 0.953 0.971 0.754 pbc3 0.959 sn1 0.968 主观规范 sn2 0.960 0.973 0.756 sn3 0.945 att1 0.968 行为态度 att2 0.961 0.972 0.767 att3 0.951 biu1 0.937 行为意向 biu2 0.922 0.930 0.762 biu3 0.924
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表 5 模型检验指标结果
Table 5. Fitness statistics of the confirmatory factor analysis
RMSEA CFI TLI SRMR 参考值 ≤0.08 ≥0.900 ≥0.900 < 0.08 模型参数 0.077 0.982 0.975 0.022
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表 6 带潜变量的随机系数Logit模型和带潜变量的多项Logit模型参数标定结果
Table 6. Estimation results of the random parameters Logit model with latent variables and hybrid choice model
变量 带潜变量的随机系数Logit模型 带潜变量的多项Logit模型 火车 无人驾驶汽车 火车 无人驾驶汽车 TT -0.014***（均值） 0.014***（标准差） -0.008*** ACC -0.008**（均值） 0.021***（标准差） TC -0.010***（均值） 0.017***（标准差） -0.003*** GEND -1.099*** -0.980*** -0.899*** -0.828*** AGE 0.726*** 0.688*** 0.596*** 0.558*** EDU 0.975*** 0.505*** 0.765*** 0.432*** OCCU -0.495*** -0.364*** -0.375*** -0.301*** HINC 0.376** 0.422*** 0.315** 0.336*** LICENSE 0.534** 0.733** 0.424** 0.533** CAR 0.729*** 0.784*** 0.605*** 0.607** PASSEN -0.633*** -0.467*** ATT 0.497*** 0.331*** PBC 0.576*** 0.355*** 对数似然值 -2
399.040-2 423.865 Pseudo R2 0.362 0.137 注：***，**，*对应的显著性水平分别为1%，5%，10%。
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表 7 出行费用和车内时间的平均直接弹性
Table 7. Average direct elasticities of travel costs and in-vehicle time
选择 火车 无人驾驶汽车 市际班车 出行费用弹性 车内时间弹性 出行费用弹性 车内时间弹性 出行费用弹性 车内时间弹性 火车 -0.441 -0.286 0.368 0.205 0.200 0.114 无人驾驶汽车 0.410 0.575 -0.403 -0.467 0.761 -0.043 市际班车 0.018 0.039 0.031 -0.002 -0.765 -0.109
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[1] FAGNANT D J, KOCKELMAN K. Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations[J]. Transportation Research Part A: Policy and Practice, 2015, 77(7): 167-181. [2] PERRINE K A, KOCKELMAN K M, HUANG Y. Anticipating long-distance travel shifts due to self-driving vehicles[J]. Journal of Transport Geography, 2020, 82(1): 102547. [3] KRÖGER L, KUHNIMHOF T, TROMMER S. Does context matter? A comparative study modelling autonomous vehicle impact on travel behaviour for Germany and the USA[J]. Transportation Research Part A: Policy and Practice, 2019, 122(4): 146-161. [4] CHEN T D, KOCKELMAN K M, HANNA J P. Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions[J]. Transportation Research Part A: Policy and Practice, 2016, 94(12): 243-254. [5] BOESCH P M, CIARI F, AXHAUSEN K W. Autonomous vehicle fleet sizes required to serve different levels of demand[J]. Transportation Research Record, 2016, 2542(1): 111-119. doi: 10.3141/2542-13 [6] LAMONDIA J J, FAGNANT D J, QU H, et al. Shifts in long-distance travel mode due to automated vehicles statewide mode-shift simulation experiment and travel survey analysis[J]. Transportation Research Record Journal of the Transportation Research Board, 2016, 2566(1): 1-11. doi: 10.3141/2566-01 [7] 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. https://www.cnki.com.cn/Article/CJFDTOTAL-XNJT202106004.htmLIU Z W, LIU J R, DENG W. Impact of autonomous vehicle on travel mode choice behavior[J]. Journal of Southwest Jiaotong University, 2021, 56(6): 1161-1168. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XNJT202106004.htm [8] 姚荣涵, 杨澜, 王仲. 考虑潜变量的自动驾驶汽车租赁行为[J]. 西南交通大学学报, 2021, 56(6): 1153-1160. https://www.cnki.com.cn/Article/CJFDTOTAL-XNJT202106003.htmYAO R H, YANG L, WANG Z. Leasing behavior for autonomous vehicles considering latent variables[J]. Journal of Southwest Jiaotong University, 2021, 56(6): 1153-1160. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XNJT202106003.htm [9] 范琪, 王炜, 杨洋, 等. 家庭收入差异对出行方式选择的影响分析[J]. 交通信息与安全, 2019, 37(6): 111-120. https://www.cnki.com.cn/Article/CJFDTOTAL-JTJS201906015.htmFAN Q, WANG W, YANG Y, et al. Effects of household income differences on travel mode choice[J]. Journal of Transport Information and Safety, 2019, 37(6): 111-120. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JTJS201906015.htm [10] BEN-AKIVA M, MCFADDEN D, TRAIN K, et al. Hybrid choice models: Progress and challenges[J]. Marketing Letters, 2002, 13(3): 163-175. doi: 10.1023/A:1020254301302 [11] SANBONMATSU D M, STRAYER D L, YU Z, et al. Cognitive underpinnings of beliefs and confidence in beliefs about fully automated vehicles[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2018, 55(2): 114-122. [12] KAUR K, RAMPERSAD G. Trust in driverless cars: Investigating key factors influencing the adoption of driverless cars[J]. Journal of Engineering and Technology Management, 2018, 48(4): 87-96. [13] LIU P, YANG R, XU Z. Public acceptance of fully automated driving: Effects of social trust and risk/benefit perceptions[J]. Risk Analysis, 2019, 39(2): 326-341. [14] JING P, HUANG H, RAN B, et al. Exploring the factors affecting mode choice intention of autonomous vehicle based on an extended theory of planned behavior: A case study in China[J]. Sustainability, 2019, 11(4): 1155. doi: 10.3390/su11041155 [15] KRUEGER R, RASHIDI T H, ROSE J M. Preferences for shared autonomous vehicles[J]. Transportation Research Part C: Emerging Technologies, 2016, 69(8): 343-355. [16] 刘建荣, 刘志伟, 任倩. 考虑出行者异质性的高铁站到达方式选择[J]. 华南理工大学学报(自然科学版), 2019, 47(9): 47-52. https://www.cnki.com.cn/Article/CJFDTOTAL-HNLG201909008.htmLIU J R, LIU Z W, REN Q. Incorporating heterogeneity into travelers' high-speed rail station arrival mode choice[J]. Journal of South China University of Technology, 2019, 47(9): 47-52. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HNLG201909008.htm [17] GKARTZONIKAS C, GKRITZA K. What have we learned? A review of stated preference and choice studies on autonomous vehicles[J]. Transportation Research Part C: Emerging Technologies, 2019, 98(1): 323-337. [18] 王灿, 王德, 朱玮, 等. 离散选择模型研究进展[J]. 地理科学进展, 2015, 34(10): 1275-1287. https://www.cnki.com.cn/Article/CJFDTOTAL-DLKJ201510008.htmWANG C, WANG D, ZHU W, et al. Research progress of discrete choice models[J]. Progress in Geography, 2015, 34(10): 1275-1287. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DLKJ201510008.htm [19] AJZEN I. The theory of planned behavior[J]. Journal of Leisure Research, 1991, 50(2): 179-211. [20] BIERNACKI C, GOVAERT G. Choosing models in model-based clustering and discriminant analysis[J]. Journal of Statistical Computation and Simulation, 1999, 64(1): 49-71. [21] YÁÑEZ M F, RAVEAU S, ORTÚZAR J D D. Inclusion of latent variables in mixed logit models: Modelling and forecasting[J]. Transportation Research Part A: Policy and Practice, 2010, 44(9): 744-753. [22] WANG J, WANG X. Structural equation modeling: Applications using Mplus[M]. New York: John Wiley & Sons, 2019. -
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