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A Demand Forecasting System for Clean-Fuel Vehicles

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  • Brownstone, David
  • Bunch, David S.
  • Golob, Thomas F.

Abstract

This paper describes an ongoing project to develop a demand forecasting model for clean-fuel vehicles in California. Large-scale surveys of both households and commercial fleet operators have been carried out. These data are being used to calibrate a new micro-simulation based vehicle demand forecasting system. Based on pre-specified attributes of future vehicles (including specified clean-fueled vehicle incentives), the system will produce annual forecasts of new and used vehicle demand by type of vehicle and geographic region. The system will also forecast annual vehicle miles traveled for all vehicles and recharging demand by time of day for electric vehicles. These results are potentially useful to utility companies in their demand-side management planning, to public agencies in their evaluation incentive schemes, and to manufacturers faced with designing and marketing clean-fuel vehicles.

Suggested Citation

  • Brownstone, David & Bunch, David S. & Golob, Thomas F., 1994. "A Demand Forecasting System for Clean-Fuel Vehicles," University of California Transportation Center, Working Papers qt79c3g7xv, University of California Transportation Center.
    • Handle: RePEc:cdl:uctcwp:qt79c3g7xv
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    References listed on IDEAS

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    1. Beggs, S. & Cardell, S. & Hausman, J., 1981. "Assessing the potential demand for electric cars," Journal of Econometrics, Elsevier, vol. 17(1), pages 1-19, September.
    2. Calfee, John E., 1985. "Estimating the demand for electric automobiles using fully disaggregated probabilistic choice analysis," Transportation Research Part B: Methodological, Elsevier, vol. 19(4), pages 287-301, August.
    3. David L. Greene, 1990. "Fuel Choice For Multi‐Fuel Vehicles," Contemporary Economic Policy, Western Economic Association International, vol. 8(4), pages 118-137, October.
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    Cited by:

    1. Golob, Thomas F. & Kim, Seyoung & Ren, Weiping, 1996. "How Households Use Different Types of Vehicles: A Structural Driver Allocation and Usage Model," University of California Transportation Center, Working Papers qt6xx6j51x, University of California Transportation Center.
    2. Golob, Thomas F. & Torous, Jane & Bradley, Mark & Brownstone, David & Crane, Soheila Soltani & Bunch, David S., 1997. "Commercial fleet demand for alternative-fuel vehicles in California," Transportation Research Part A: Policy and Practice, Elsevier, vol. 31(3), pages 219-233, May.
    3. Habibi, Shiva & Sundberg, Marcus & Karlström, Anders, 2013. "An empirical study of predicting car type choice in Sweden using cross-validation and feature-selection," Working papers in Transport Economics 2013:13, CTS - Centre for Transport Studies Stockholm (KTH and VTI), revised 23 Apr 2014.
    4. Golob, Thomas F. & Kim, Seyoung & Ren, Weiping, 1994. "A Structural Model of Vehicle Use in Two-Vehicle Households," University of California Transportation Center, Working Papers qt9wp6c79q, University of California Transportation Center.
    5. Mallikarjun Patil & Bandhan Bandhu Majumdar & Prasanta Kumar Sahu & Long T. Truong, 2021. "Evaluation of Prospective Users’ Choice Decision toward Electric Two-Wheelers Using a Stated Preference Survey: An Indian Perspective," Sustainability, MDPI, vol. 13(6), pages 1-22, March.
    6. 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.
    7. Susanne Linder, 2011. "Spatial diffusion of electric vehicles in the German metropolitan region of Stuttgart," ERSA conference papers ersa11p557, European Regional Science Association.
    8. Golob, Thomas F. & Kim, Seyoung & Ren, Weiping, 1996. "How households use different types of vehicles: A structural driver allocation and usage model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(2), pages 103-118, March.
    9. Bastani, Parisa & Heywood, John B. & Hope, Chris, 2012. "The effect of uncertainty on US transport-related GHG emissions and fuel consumption out to 2050," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(3), pages 517-548.

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