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Volume 57 Issue 4
Jul.  2022
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Journal of Southwest Jiaotong University  > 2022  >  57(4): 745-752.
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
shu

Choice Behavior of Time-Sharing Vehicle Leasing Considering Individual Heterogeneity

doi: 10.3969/j.issn.0258-2724.20200428
  • 1.

    School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China

  • 2.

    Western Branch of China Academy of Urban Planning and Design, Chongqing 401121, China

  • Received Date: 14 Jul 2020
  • Rev Recd Date: 03 Mar 2021
  • Publish Date: 15 Apr 2021
    Abstract

  • 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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