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Understanding the generation mechanism of BEV drivers' charging demand: An exploration of the relationship between charging choice and complexity of trip chaining patterns

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  • Zhang, Yiyuan
  • Luo, Xia
  • Qiu, Yuansen
  • Fu, Yuxue

Abstract

In the context of the rapid popularization of battery electric vehicles (BEV), it has become a key problem to deeply understand the charging demand and reasonably configure charging facilities. Exploring the relationship between trip chaining patterns and charging choice of battery electric vehicle users may aid in understanding the mechanism of charging demand. Based on the recursive simultaneous bivariate probit model, we integrate the impact of BEV users’ risk aversion attitude and develop two causal structures: one is that the charging choice is determined first and influences trip chaining pattern, the other is that trip chaining pattern is determined first and influences charging choice. Next, a stated preference survey is conducted through online platform and field survey, and a total of 494 valid questionnaires are collected. The model results show that the fitting effect of the causal structure model where trip chaining pattern precedes charging choice is better than another causal structure model. Moreover, integrating the influence of risk aversion attitude in the form of latent variable into the RSBP model can significantly improve the fit goodness of the model. These findings will help to further understand the mechanism of charging demand and have some implications on estimating charging demand.

Suggested Citation

  • Zhang, Yiyuan & Luo, Xia & Qiu, Yuansen & Fu, Yuxue, 2022. "Understanding the generation mechanism of BEV drivers' charging demand: An exploration of the relationship between charging choice and complexity of trip chaining patterns," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 110-126.
  • Handle: RePEc:eee:transa:v:158:y:2022:i:c:p:110-126
    DOI: 10.1016/j.tra.2022.02.007
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    References listed on IDEAS

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    1. Md Hadiuzzaman & Nahid Parvez Farazi & Sanjana Hossain & Saurav Barua & Farzana Rahman, 2019. "Structural equation approach to investigate trip-chaining and mode choice relationships in the context of developing countries," Transportation Planning and Technology, Taylor & Francis Journals, vol. 42(4), pages 391-415, May.
    2. Soto, Jose J. & Márquez, Luis & Macea, Luis F., 2018. "Accounting for attitudes on parking choice: An integrated choice and latent variable approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 65-77.
    3. Roberto Roca Paz & Silke Uebelmesser, 2021. "Risk attitudes and migration decisions," Journal of Regional Science, Wiley Blackwell, vol. 61(3), pages 649-684, June.
    4. Li, Meng & Jia, Yinghao & Shen, Zuojun & He, Fang, 2017. "Improving the electrification rate of the vehicle miles traveled in Beijing: A data-driven approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 97(C), pages 106-120.
    5. Huang, Yuqiao & Gao, Linjie & Ni, Anning & Liu, Xiaoning, 2021. "Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai, China," Journal of Transport Geography, Elsevier, vol. 93(C).
    6. Bhat, Chandra R., 2015. "A new generalized heterogeneous data model (GHDM) to jointly model mixed types of dependent variables," Transportation Research Part B: Methodological, Elsevier, vol. 79(C), pages 50-77.
    7. Hensher,David A. & Rose,John M. & Greene,William H., 2015. "Applied Choice Analysis," Cambridge Books, Cambridge University Press, number 9781107465923.
    8. David Hensher, 2013. "Exploring the relationship between perceived acceptability and referendum voting support for alternative road pricing schemes," Transportation, Springer, vol. 40(5), pages 935-959, September.
    9. Xu, Min & Meng, Qiang & Liu, Kai & Yamamoto, Toshiyuki, 2017. "Joint charging mode and location choice model for battery electric vehicle users," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 68-86.
    10. Cavadas, Joana & Homem de Almeida Correia, Gonçalo & Gouveia, João, 2015. "A MIP model for locating slow-charging stations for electric vehicles in urban areas accounting for driver tours," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 75(C), pages 188-201.
    11. Nie, Yu (Marco) & Ghamami, Mehrnaz, 2013. "A corridor-centric approach to planning electric vehicle charging infrastructure," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 172-190.
    12. Ye, Xin & Pendyala, Ram M. & Gottardi, Giovanni, 2007. "An exploration of the relationship between mode choice and complexity of trip chaining patterns," Transportation Research Part B: Methodological, Elsevier, vol. 41(1), pages 96-113, January.
    Full references (including those not matched with items on IDEAS)

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