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Acceptability, energy consumption, and costs of electric vehicle for ride-hailing drivers in Beijing


  • Tu, Wei
  • Santi, Paolo
  • Zhao, Tianhong
  • He, Xiaoyi
  • Li, Qingquan
  • Dong, Lei
  • Wallington, Timothy J.
  • Ratti, Carlo


The acceptability, energy consumption, and environmental benefits of electric vehicles are highly dependent on travel patterns. With increasing ride-hailing popularity in mega-cities, urban mobility patterns are greatly changing; therefore, an investigation of the extent to which electric vehicles would satisfy the needs of ride-hailing drivers becomes important to support sustainable urban growth. A first step in this direction is reported here. GPS-trajectories of 144,867 drivers over 104 million km in Beijing were used to quantify the potential acceptability, energy consumption, and costs of ride-hailing electric vehicle fleets. Average daily travel distance and travel time for ride-hailing drivers was determined to be 129.4 km and 5.7 h; these values are substantially larger than those for household drivers (40.0 km and 1.5 h). Assuming slow level-1 (1.8 KW) or moderate level-2 (7.2 KW) charging is available at all home parking locations, battery electric vehicles with 200 km all electric range (BEV200) could be used by up to 47% or 78% of ride-hailing drivers and electrify up to 20% or 55% of total distance driven by the ride-hailing fleet. With level-2 charging available at home, work, and public parking, the acceptance ceiling increases to up to 91% of drivers and 80% of distance. Our study suggests that long range BEVs and widespread level-2 charging infrastructure are needed for large-scale electrification of ride-hailing mobility in Beijing. The marginal benefits of increased all electric range, effects on charging infrastructure distribution, and payback times are also presented and discussed. Given the observed heterogeneity of ride-hailing vehicle travel, our study outlines the importance of individual-level analysis to understand the electrification potential and future benefits of electric vehicles in the era of shared smart transportation.

Suggested Citation

  • Tu, Wei & Santi, Paolo & Zhao, Tianhong & He, Xiaoyi & Li, Qingquan & Dong, Lei & Wallington, Timothy J. & Ratti, Carlo, 2019. "Acceptability, energy consumption, and costs of electric vehicle for ride-hailing drivers in Beijing," Applied Energy, Elsevier, vol. 250(C), pages 147-160.
  • Handle: RePEc:eee:appene:v:250:y:2019:i:c:p:147-160
    DOI: 10.1016/j.apenergy.2019.04.157

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    References listed on IDEAS

    1. Zarar Siddiqi & Ron Buliung, 2013. "Dynamic ridesharing and information and communications technology: past, present and future prospects," Transportation Planning and Technology, Taylor & Francis Journals, vol. 36(6), pages 479-498, August.
    2. Yu, Biying & Ma, Ye & Xue, Meimei & Tang, Baojun & Wang, Bin & Yan, Jinyue & Wei, Yi-Ming, 2017. "Environmental benefits from ridesharing: A case of Beijing," Applied Energy, Elsevier, vol. 191(C), pages 141-152.
    3. Dijk, Marc & Orsato, Renato J. & Kemp, René, 2013. "The emergence of an electric mobility trajectory," Energy Policy, Elsevier, vol. 52(C), pages 135-145.
    4. De Gennaro, Michele & Paffumi, Elena & Scholz, Harald & Martini, Giorgio, 2014. "GIS-driven analysis of e-mobility in urban areas: An evaluation of the impact on the electric energy grid," Applied Energy, Elsevier, vol. 124(C), pages 94-116.
    5. Daina, Nicolò & Sivakumar, Aruna & Polak, John W., 2017. "Modelling electric vehicles use: a survey on the methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 447-460.
    6. Kelly, Jarod C. & MacDonald, Jason S. & Keoleian, Gregory A., 2012. "Time-dependent plug-in hybrid electric vehicle charging based on national driving patterns and demographics," Applied Energy, Elsevier, vol. 94(C), pages 395-405.
    7. Judd Cramer & Alan B. Krueger, 2016. "Disruptive Change in the Taxi Business: The Case of Uber," American Economic Review, American Economic Association, vol. 106(5), pages 177-182, May.
    8. Shepero, Mahmoud & Munkhammar, Joakim, 2018. "Spatial Markov chain model for electric vehicle charging in cities using geographical information system (GIS) data," Applied Energy, Elsevier, vol. 231(C), pages 1089-1099.
    9. Vazifeh, Mohammad M. & Zhang, Hongmou & Santi, Paolo & Ratti, Carlo, 2019. "Optimizing the deployment of electric vehicle charging stations using pervasive mobility data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 75-91.
    10. Jiang, C.X. & Jing, Z.X. & Cui, X.R. & Ji, T.Y. & Wu, Q.H., 2018. "Multiple agents and reinforcement learning for modelling charging loads of electric taxis," Applied Energy, Elsevier, vol. 222(C), pages 158-168.
    11. Wolbertus, Rick & Kroesen, Maarten & van den Hoed, Robert & Chorus, Caspar, 2018. "Fully charged: An empirical study into the factors that influence connection times at EV-charging stations," Energy Policy, Elsevier, vol. 123(C), pages 1-7.
    12. He, Xiaoyi & Wu, Ye & Zhang, Shaojun & Tamor, Michael A. & Wallington, Timothy J. & Shen, Wei & Han, Weijian & Fu, Lixin & Hao, Jiming, 2016. "Individual trip chain distributions for passenger cars: Implications for market acceptance of battery electric vehicles and energy consumption by plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 180(C), pages 650-660.
    13. Ke, Wenwei & Zhang, Shaojun & He, Xiaoyi & Wu, Ye & Hao, Jiming, 2017. "Well-to-wheels energy consumption and emissions of electric vehicles: Mid-term implications from real-world features and air pollution control progress," Applied Energy, Elsevier, vol. 188(C), pages 367-377.
    14. Sun, Lishan & Wang, Shunchao & Liu, Shuli & Yao, Liya & Luo, Wei & Shukla, Ashish, 2018. "A completive research on the feasibility and adaptation of shared transportation in mega-cities – A case study in Beijing," Applied Energy, Elsevier, vol. 230(C), pages 1014-1033.
    15. Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
    16. Plötz, Patrick & Jakobsson, Niklas & Sprei, Frances, 2017. "On the distribution of individual daily driving distances," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 213-227.
    17. Tu, Wei & Cao, Rui & Yue, Yang & Zhou, Baoding & Li, Qiuping & Li, Qingquan, 2018. "Spatial variations in urban public ridership derived from GPS trajectories and smart card data," Journal of Transport Geography, Elsevier, vol. 69(C), pages 45-57.
    18. Brady, John & O’Mahony, Margaret, 2016. "Development of a driving cycle to evaluate the energy economy of electric vehicles in urban areas," Applied Energy, Elsevier, vol. 177(C), pages 165-178.
    19. Khan, Mobashwir & Kockelman, Kara M., 2012. "Predicting the market potential of plug-in electric vehicles using multiday GPS data," Energy Policy, Elsevier, vol. 46(C), pages 225-233.
    20. Björnsson, Lars-Henrik & Karlsson, Sten, 2015. "Plug-in hybrid electric vehicles: How individual movement patterns affect battery requirements, the potential to replace conventional fuels, and economic viability," Applied Energy, Elsevier, vol. 143(C), pages 336-347.
    21. Fischer, David & Harbrecht, Alexander & Surmann, Arne & McKenna, Russell, 2019. "Electric vehicles’ impacts on residential electric local profiles – A stochastic modelling approach considering socio-economic, behavioural and spatial factors," Applied Energy, Elsevier, vol. 233, pages 644-658.
    22. Grüger, Fabian & Dylewski, Lucy & Robinius, Martin & Stolten, Detlef, 2018. "Carsharing with fuel cell vehicles: Sizing hydrogen refueling stations based on refueling behavior," Applied Energy, Elsevier, vol. 228(C), pages 1540-1549.
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    Cited by:

    1. Qiang Xing & Zhong Chen & Ziqi Zhang & Xiao Xu & Tian Zhang & Xueliang Huang & Haiwei Wang, 2020. "Urban Electric Vehicle Fast-Charging Demand Forecasting Model Based on Data-Driven Approach and Human Decision-Making Behavior," Energies, MDPI, Open Access Journal, vol. 13(6), pages 1-32, March.


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