Identifying the Determinants of Travelers Choosing Electric Vehicles in the Context of Intercity Travel
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摘要: 受限于电动汽车的电池技术及由此带来的里程焦虑，电动汽车的主要应用场景依旧是城市内部出行。为了扩大电动汽车的使用，有必要研究城际出行场景下出行者对电动汽车的选择行为。本研究采用意向调查方法，设计了城际出行场景下出行者选择电动汽车和燃油汽车的调查问卷，基于回收的400份有效问卷构建了混合Logit模型，分析了出行者对电动汽车的选择偏好及其异质性，并在此基础之上探讨了出行者的时间价值和电动车续航里程的弹性，最终提出了相应的政策建议以扩大电动汽车的市场份额和应用场景。研究结果表明：①实验所考虑的因素会显著影响出行者城际出行场景下的选择行为，其中高速里程占比偏好系数分别服从μ =-0.473、σ =0.818（电动汽车）以及μ =-0.576、σ =1.371（燃油汽车）的正态分布，电动汽车的拥挤行驶时间和续航里程的偏好系数分别服从μ =0.397、σ =0.422的负对数正态分布以及μ =-1.053、σ=0.356的对数正态分布；②对于电动汽车而言，时间价值最大的场景是拥挤行驶（133.16元/h），其后依次为充电排队（71.83元/h）、充电（54.05元/h）和自由行驶（52.50元/h）；对于燃油汽车而言，时间价值最大的场景是加油排队（453.43元/h），其后依次为拥挤行驶（159.14元/h）、自由行驶（60.57元/h）和加油（54.05元/h）；③电动汽车续航里程每增加1%，本研究案例中电动汽车的分担率将增加0.17%。Abstract: Due to the current battery techniques and travelers' range anxiety, the electric vehicles are most commonly used in the context of intra-city travel. In order to improve the usage of electric vehicles, it is necessary to investigate travelers' choice toward electric vehicles in the context of intercity travel. To this end, this study conducted a survey based on a stated preference experiment, where the respondents are requested to choose electric vehicles or fuel vehicles based on the given travel context. 400 valid questionnaires are finally collected. Next, a mixed Logit model is established and travelers' heterogeneous preferences toward electric vehicles are analyzed, based on which travelers' value of time and the elasticity of electric vehicles' endurance mileage are computed and related policy implications aiming to increase the market share and usage of electric vehicles are put forward. The research results show the following conclusions: ①The considered attributes in the experiment significantly influence travelers' choosing electric vehicles for intercity travel. Specifically, the taste parameter of ratio of freeway follows a normal distribution with μ =-0.473 and σ =0.818 for electric vehicles and a normal distribution with μ =-0.576 and σ = 1.371 for fossil-fueled vehicles, respective; the taste parameter of congested travel time for electric vehicles and endurance mileage follow negative log-normal distribution with μ =0.397 and σ =0.422 and log-normal distribution with μ =-1.053 and σ =0.356, respectively. ②In terms of electric vehicles, travelers have largest value of time when congested traveling (133.16 CNY/h), followed by those when queueing for charging (71.83 CNY/h), charging (54.05 CNY/h) and free traveling (52.50 CNY/h) in sequence. In terms of fossil-fueled vehicles, travelers have largest value of time when queueing for fueling (453.43 CNY/h), followed by those when congested traveling (159.14 CNY/h), free traveling (60.57 Yuan/h) and fueling (54.05 CNY/h) in sequence. ③If electric vehicles' endurance mileages are increased by 1%, its corresponding market share of sample in this case study would be increased by 0.17%.
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Key words:
- traffic planning /
- electric vehicle /
- intercity travel /
- preference heterogeneity /
- mixed Logit model /
- SP survey
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图 1 观测数据中电动汽车和燃油汽车的选择比例
Figure 1. Ratio of electric vehicles and oil-fueled vehicles in the observations
图 4 电动汽车拥挤行驶场景下出行者的时间价值分布
Figure 4. Distribution of value of time for electric vehicles when congested traveling
表 1 意向调查属性及水平设置
Table 1. Attributes and corresponding levels in SP survey
属性 水平1 水平2 水平3 水平4 电动汽车续航里程/km 200 400 出行距离/km 100 200 300 400 平均充电站/加油站间距/km 20 30 40 50 平均充电站排队时间/min 不排队 10 20 30 平均加油站排队时间/min 不排队 5 平均充电时间/min 15 20 25 30 每百公里电费/元 10 20 每百公里油费/元 50 60 高速公路里程占比/% 0 25 50 75 每百公里高速费用/元 0 20 40 60 拥堵时间/min 0 10 20 30 
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表 2 基于意向调查实验的情景选择题（示例）
Table 2. An example of choice scenarios based on the SP experiment
属性 电动汽车 燃油汽车 续航里程/km 400 500 充电/加油排队时间/min 20 不排队 充电/加油时长/min 15 5 高速公路占总里程比例/\% 75 75 需支付高速费用/元 90 90 总油耗/电费/元 20 120 自由行驶时间/min 140 140 拥挤行驶时间/min 20 不拥堵 出行距离/km 200 200 充电/加油平均站间距离/km 20 20
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表 3 受访者个人基本属性描述性统计
Table 3. Descriptive statistics of respondents' socio-demographics
属性名称 属性 百分比/% 性别 男 45.00 女 55.00 ≤30 47.50 年龄/岁 ＞30~40 40.75 ＞40~60 11.75 单身 33.75 婚姻状况 已婚，但无小孩 9.50 已婚，且有小孩 56.75 高中及以下 6.75 大专 17.25 受教育程度 本科 67.25 硕士 7.75 博士及以上 1.00 ≤4 000 18.50 月收入（税后）/元 ＞4 000~10 000 51.00 ＞10 000~30000 26.50 ＞30000 4.00
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表 4 模型估计结果
Table 4. Model estimation results
变量类型 汽车类型 模型参数 MNL模型 混合Logit模型 估计值 标准误 t值 P值 估计值 标准误 t值 P值 常数项 电动汽车 （μ） 0.000 0.000 燃油汽车 （μ） -1.931 0.193 -10.012 0.000 -2.371 0.267 -8.866 0.000 充电排队 电动汽车 （μ） -0.703 0.149 -4.708 0.000 -0.877 0.170 -5.146 0.000 时间/h 燃油汽车 （μ） -2.525 0.670 -3.770 0.000 -3.174 0.755 -4.207 0.000 电动汽车 （μ） -0.476 0.200 -2.381 0.017 -0.473 0.239 -1.984 0.047 高速里程 （σ） 0.818 0.203 4.040 0.000 占比/% 燃油汽车 （μ） -0.408 0.196 -2.083 0.037 -0.576 0.247 -2.330 0.020 （σ） 1.371 0.187 7.312 0.000 总费用 电动汽车 （μ） -0.954 0.111 -8.578 0.000 -1.221 0.127 -9.624 0.000 （元） 燃油汽车 （μ） -0.536 0.106 -5.054 0.000 -0.700 0.123 -5.708 0.000 选项 自由行 电动汽车 （μ） -0.536 0.112 -4.795 0.000 -0.641 0.132 -4.863 0.000 属性 驶时间/h 燃油汽车 （μ） -0.374 0.100 -3.744 0.000 -0.424 0.120 -3.524 0.000 固定系数 -1.294 0.151 -8.594 0.000 拥挤行 电动汽车 （μ） 0.397 0.140 2.844 0.005 驶时/h （σ） 0.422 0.139 3.040 0.002 燃油汽车 （μ） -0.886 0.151 -5.858 0.000 -1.114 0.170 -6.544 0.000 固定系数 -1.294 0.151 -8.594 0.000 续航里程（100 km） 电动汽车 (μ) 0.397 0.140 2.844 0.005 （σ） 0.422 0.139 3.040 0.002 充电时间/h 电动汽车 （μ） -0.455 0.298 -1.524 0.128 -0.660 0.337 -1.959 0.050 电动汽车 不了解 0.000 0.000 个人属性（针对电动汽车效用） 熟悉度 电动汽车 了解 0.272 0.069 3.921 0.000 0.432 0.162 2.663 0.008 燃油车保有 电动汽车 无 0.000 0.000 有 -0.281 0.068 -4.125 0.000 -0.321 0.156 -2.057 0.040 电动汽车保有 电动汽车 无 0.000 0.000 有 0.339 0.062 5.503 0.000 0.365 0.140 2.604 0.009 面板效应(σ) 0.974 0.078 12.431 0.000 受访人数 400 400 观测数据量 6400 6400 初始似然值 -4436.142 -4436.142 收敛似然值 -3787.210 -3502.014 待估系数个数 16 21 ρ2 0.146 0.211 修正ρ2 0.143 0.206
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表 5 各场景下出行者的时间价值（均值）
Table 5. Travelers' value of time in different situations(mean)
场景 车辆种类 时间价值/（元/h） 时间价值/（元/min） 充电/加油排队 电动汽车 71.83 1.20 燃油汽车 453.43 7.56 自由行驶 电动汽车 52.50 0.87 燃油汽车 60.57 1.01 拥挤行驶 电动汽车 133.16 2.22 燃油汽车 159.14 2.65 充电 电动汽车 54.05 0.90
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[1] 沈峰, 马铁驹, 赵兴荣, 等. 激励政策退坡下私家电动汽车扩散分析-以上海市为例[J]. 管理科学学报, 2021, 24(9): 79-104.SHEN F, MA T J, ZHAO X R, et al. Diffusion of private electric vehicles with the fade-out of incentive policies: the case of Shanghai[J]. Journal of Management Sciences in China, 2021, 24(9): 79-104. (in Chinese) [2] 陆欢, 干宏程, 王馨玉, 等. 考虑心理因素及社会网络影响的电动汽车购买意愿研究[J]. 交通运输研究, 2022, 8(2): 49-56.LU H, GAN H C, WANG X Y, et al. Purchase intention of electric vehicles considering influence of psychological factors and social network[J]. Transport Research, 2022, 8(2): 49-56. (in Chinese) [3] MAYBURY L, CORCORAN P, CIPCIGAN L. Mathematical modelling of electric vehicle adoption: a systematic literature review[J]. Transportation Research Part D: Transport and Environment, 2022, 107: 103278. doi: 10.1016/j.trd.2022.103278 [4] 鲍琼, 谭旭, 屈琦凯, 等. 基于用户时空活动与模糊决策的电动汽车充电需求预测[J]. 东南大学学报(自然科学版), 2022, 52(6): 1209-1218.BAO Q, TAN X, QU Q K, et al. Prediction of electric vehicle charging demand based on user space-time activities and fuzzy decision-making[J]. Journal of Southeast University (Natural Science Edition), 2022, 52(6): 1209-1218. (in Chinese) [5] JIA Q S, LONG T. A review on charging behavior of electric vehicles: data, model, and control[J]. Control Theory and Technology, 2020, 18(3): 217-230. doi: 10.1007/s11768-020-0048-8 [6] LANGBROEK J H M, FRANKLIN J P, SUSILO Y O. The effect of policy incentives on electric vehicle adoption[J]. Energy Policy, 2016, 94: 94-103. doi: 10.1016/j.enpol.2016.03.050 [7] HULL C, GILIOMEE J H, VISSER M, et al. Electric vehicle adoption intention among paratransit owners and drivers in South Africa[J]. Transport Policy, 2024, 146: 137-149. doi: 10.1016/j.tranpol.2023.11.015 [8] KIM J, RASOULI S, TIMMERMANS H. Expanding scope of hybrid choice models allowing for mixture of social influences and latent attitudes: Application to intended purchase of electric cars[J]. Transportation Research Part A: Policy and Practice, 2014, 69: 71-85. doi: 10.1016/j.tra.2014.08.016 [9] KUMAR R R, CHAKRABORTY A, MANDAL P. Promoting electric vehicle adoption: who should invest in charging infrastructure?[J]. Transportation Research Part E: Logistics and Transportation Review, 2021, 149: 102295. doi: 10.1016/j.tre.2021.102295 [10] 黄瑞锦, 顾高峰. 基于混合Logit模型的电动汽车购买意愿影响因素研究[J]. 交通运输研究, 2021, 7(1): 95-103.HUANG R J, GU G F. Factors impacting purchasing intentions to electric vehicles based on mixed logit model[J]. Transport Research, 2021, 7(1): 95-103. (in Chinese) [11] JENSEN A F, MABIT S L. The use of electric vehicles: a case study on adding an electric car to a household[J]. Transportation Research Part A: Policy and Practice, 2017, 106: 89-99. doi: 10.1016/j.tra.2017.09.004 [12] 梁露, 韩飞. 考虑排队时间和充电费用的电动汽车充电站选址模型[J]. 交通信息与安全, 2023, 41(4): 154-162. doi: 10.3963/j.jssn.1674-4861.2023.04.016LIANG L, HAN F. A site selection model for electric vehicle charging stations considering queuing time and charging cost[J]. Journal of Transport Information and Safety, 2023, 41 (4): 154-162. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2023.04.016 [13] ANDERSON J E, BERGFELD M, NGUYEN D M, et al. Real-world charging behavior and preferences of electric vehicles users in Germany[J]. International Journal of Sustainable Transportation, 2023, 17(9): 1032-1046. doi: 10.1080/15568318.2022.2147041 [14] 王立晓, 周娅. 考虑心理潜变量及异质性的电动汽车联合充电选择行为研究[J]. 合肥工业大学学报(自然科学版), 2022, 45(9): 1216-1224.WANG L X, ZHOU Y. A study on joint charging choice behavior for electric vehicles considering psychological latent variables and heterogeneity[J]. Journal of Hefei University of Technology(Natural Science), 2022, 45(9): 1216-1224. (in Chinese) [15] LOEB B, KOCKELMAN K M, LIU J. Shared autonomous electric vehicle(SAEV)operations across the Austin, Texas network with charging infrastructure decisions[J]. Transportation Research Part C: Emerging Technologies, 2018, 89: 222-233. doi: 10.1016/j.trc.2018.01.019 [16] JIN F, AN K, YAO E. Mode choice analysis in urban transport with shared battery electric vehicles: a stated-preference case study in Beijing, China[J]. Transportation Research Part A: Policy and Practice, 2020, 133: 95-108. doi: 10.1016/j.tra.2020.01.009 [17] THURNER T, FURSOV K, NEFEDOVA A. Early adopters of new transportation technologies: attitudes of Russia's population towards car sharing, the electric car and autonomous driving[J]. Transportation Research Part A: Policy and Practice, 2022, 155: 403-417. doi: 10.1016/j.tra.2021.11.006 [18] HENSHER D A, ROSE J M, GREENE W H. Applied choice analysis(second edition)[M]. New York: Cambridge University Press, 2015. [19] 邵春福. 交通规划原理(第二版)[M]. 北京: 中国铁道出版社, 2015.SHAO C F. Traffic Planning(second edition)[M]. Beijing: China Railway Publishing House, 2015. (in Chinese) [20] TRAIN K. Discrete choice methods with simulation(second edition)[M]. New York: Cambridge University Press, 2009. [21] BHAT C R. Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences[J]. Transportation Research Part B: Methodological, 2003, 37(9): 837-855. doi: 10.1016/S0191-2615(02)00090-5 [22] HESS S, BIERLAIRE M, POLAK J W. Estimation of value of travel-time savings using mixed logit models[J]. Transportation Research Part A: Policy and Practice, 2005, 39(2-3): 221-236. doi: 10.1016/j.tra.2004.09.007 [23] CIRILLO C, AXHAUSEN K W. Evidence on the distribution of values of travel time savings from a six-week diary[J]. Transportation Research Part A: Policy and Practice, 2006, 40(5): 444-457. doi: 10.1016/j.tra.2005.06.007 [24] LOUVIERE J J, HENSHER D A, SWAIT J D. Stated choice methods: analysis and applications[M]. New York: Cambridge University Press, 2000. -
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