• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 57 Issue 4
Jul.  2022
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Article Contents
YANG Fei, HOU Zongting, WANG Liang, WU Haitao. Choice Behavior of Time-Sharing Vehicle Leasing Considering Individual Heterogeneity[J]. Journal of Southwest Jiaotong University, 2022, 57(4): 745-752. doi: 10.3969/j.issn.0258-2724.20200428
Citation: YANG Fei, HOU Zongting, WANG Liang, WU Haitao. Choice Behavior of Time-Sharing Vehicle Leasing Considering Individual Heterogeneity[J]. Journal of Southwest Jiaotong University, 2022, 57(4): 745-752. doi: 10.3969/j.issn.0258-2724.20200428

Choice Behavior of Time-Sharing Vehicle Leasing Considering Individual Heterogeneity

doi: 10.3969/j.issn.0258-2724.20200428
  • Received Date: 14 Jul 2020
  • Rev Recd Date: 03 Mar 2021
  • Publish Date: 15 Apr 2021
  • Lack of individual heterogeneity in traditional travel behavior models causes errors in the interpretation of real choice behaviors. In order to explore the influence of individual heterogeneity on travel choice behavior, firstly, a mixed logit based choice model and a latent-class conditional logit based choice model are built. Secondly, orthogonal design method is used to generate stated preference questionnaires for an empirical survey in Chengdu regarding travel choice behaviors of time-sharing lease on new energy vehicles. Finally, the mixed logit model is calibrated by using maximum likelihood simulation and Halton sequence sampling. The latent-class condition logit model is solved by regression analysis. The results show that access time, waiting time, in-vehicle time and cost are the key factors in choosing urban traffic modes. Both two models reveal that individual heterogeneity has a significant influence on travelers’ choice behaviors. The latent-class conditional logit model has a higher goodness of fit of 0.143 and a hit ratio of 77.85%, compared to those of 0.139 and 61.28% for the mixed logit model. Besides, the latent-class conditional logit model divides travelers into three categories, and the degree of differentiation is 0.908 4. Group 1 is most sensitive to cost but insensitive to waiting time; group 2 is more sensitive to access time and waiting time than cost; group 3 has an intermediate sensitivity to time and cost.

     

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  • [1]
    BALBONTIN C, HENSHER D A, COLLINS A T. How to better represent preferences in choice models: the contributions to preference heterogeneity attributable to the presence of process heterogeneity[J]. Transportation Research Part B: Methodological, 2019, 122: 218-248. doi: 10.1016/j.trb.2019.02.007
    [2]
    HESS S, STATHOPOULOS A, DALY A. Allowing for heterogeneous decision rules in discrete choice models: an approach and four case studies[J]. Transportation, 2012, 39(3): 565-591. doi: 10.1007/s11116-011-9365-6
    [3]
    MANNERING F L, SHANKAR V, BHAT C R. Unobserved heterogeneity and the statistical analysis of highway accident data[J]. Analytic Methods in Accident Research, 2016, 11: 1-16. doi: 10.1016/j.amar.2016.04.001
    [4]
    IBEAS A, DELL’OLIO L, BORDAGARAY M, et al. Modelling parking choices considering user heterogeneity[J]. Transportation Research Part A: Policy and Practice, 2014, 70: 41-49. doi: 10.1016/j.tra.2014.10.001
    [5]
    DUAN L W, PENG Q Y, TANG Y Y. Railway shippers’ heterogeneous preferences with random parameters latent class model[J]. Transportation Research Procedia, 2017, 25: 416-424. doi: 10.1016/j.trpro.2017.05.419
    [6]
    赵鹏,翟茹雪,宋文波. 考虑个体异质性的高速铁路旅客选择行为[J]. 北京交通大学学报,2019,43(2): 117-123. doi: 10.11860/j.issn.1673-0291.20180127

    ZHAO Peng, ZHAI Ruxue, SONG Wenbo. Passenger choice behavior of high-speed railway considering individual heterogeneity[J]. Journal of Beijing Jiaotong University, 2019, 43(2): 117-123. doi: 10.11860/j.issn.1673-0291.20180127
    [7]
    GREENE W H, HENSHER D A. A latent class model for discrete choice analysis: contrasts with mixed logit[J]. Transportation Research Part B: Methodological, 2003, 37(8): 681-698. doi: 10.1016/S0191-2615(02)00046-2
    [8]
    GREENE W H, HENSHER D A. Revealing additional dimensions of preference heterogeneity in a latent class mixed multinomial logit model[J]. Applied Economics, 2013, 45(14): 1897-1902. doi: 10.1080/00036846.2011.650325
    [9]
    刘志伟,刘建荣,邓卫. 考虑潜在类别的市内机动化出行行为模型[J]. 西南交通大学学报,2021,56(1): 131-137.

    LIU Zhiwei, LIU Jianrong, DENG Wei. Inclusion of latent class in behavior model of motorized travel in city[J]. Journal of Southwest Jiaotong University, 2021, 56(1): 131-137.
    [10]
    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.

    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.
    [11]
    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.

    YAO Ronghan, YANG Lan, WANG Zhong. Leasing behavior for autonomous vehicles considering latent variables[J]. Journal of Southwest Jiaotong University, 2021, 56(6): 1153-1160.
    [12]
    HURTUBIA R, NGUYEN M H, GLERUM A, et al. Integrating psychometric indicators in latent class choice models[J]. Transportation Research Part A: Policy and Practice, 2014, 64: 135-146. doi: 10.1016/j.tra.2014.03.010
    [13]
    BAE Y, KIM J, CHUNG J. Psychological traits to eco-friendly transportation systems:latent class approach[J]. Transportation Research Procedia, 2017, 25: 4270-4284. doi: 10.1016/j.trpro.2017.05.246
    [14]
    艾瑞咨询. 2019年中国分时租赁行业研究报告 [R/OL]. (2019-03-20) [2020-06-22]. http://report.iresearch.cn/wx/report.aspx?id=3347.
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