Volume 43 Issue 4
Aug.  2025
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HOU Gen, TIAN Lun, PAN Xiaofeng, XUE Xiaowei. Identifying the Determinants of Travelers Choosing Electric Vehicles in the Context of Intercity Travel[J]. Journal of Transport Information and Safety, 2025, 43(4): 139-148. doi: 10.3963/j.jssn.1674-4861.2025.04.014
Citation: HOU Gen, TIAN Lun, PAN Xiaofeng, XUE Xiaowei. Identifying the Determinants of Travelers Choosing Electric Vehicles in the Context of Intercity Travel[J]. Journal of Transport Information and Safety, 2025, 43(4): 139-148. doi: 10.3963/j.jssn.1674-4861.2025.04.014

Identifying the Determinants of Travelers Choosing Electric Vehicles in the Context of Intercity Travel

doi: 10.3963/j.jssn.1674-4861.2025.04.014
  • Received Date: 2024-07-10
  • 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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  • [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.1674-4861.2023.04.016

    LIANG 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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