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A Transactions Choice Model for Forecasting Demand for Alternative-Fuel Vehicles

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Author Info

  • Brownstone, David
  • Bunch, David S
  • Golob, Thomas F
  • Ren, Weiping
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    Abstract

    The vehicle choice model developed here is one component in a micro-simulation demand forecasting system being designed to produce annual forecasts of new and used vehicle demand by vehicle type and geographic area in California. The system will also forecast annual vehicle miles traveled for all vehicles and recharging demand by time of day for electric vehicles. The choice model specification differs from past studies by directly modeling vehicle transactions rather than vehicle holdings. The model is calibrated using stated preference data from a new study of 4,747 urban California households. These results are potentially useful to public transportation and energy agencies in their evaluation of alternatives to current gasoline-powered vehicles. The findings are also useful to manufacturers faced with designing and marketing alternative-fuel vehicles as well as to utility companies who need to develop long-run demand-side management planning strategies.

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    Bibliographic Info

    Paper provided by University of California Transportation Center in its series University of California Transportation Center, Working Papers with number qt3sm7w9zk.

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    Date of creation: 01 Jan 1996
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    Handle: RePEc:cdl:uctcwp:qt3sm7w9zk

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    Related research

    Keywords: Urban Studies and Planning;

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    Cited by:
    1. Achtnicht, Martin, 2011. "Do environmental benefits matter? Evidence from a choice experiment among house owners in Germany," Ecological Economics, Elsevier, vol. 70(11), pages 2191-2200, September.
    2. Daziano, Ricardo A. & Achtnicht, Martin, 2012. "Forecasting adoption of ultra-low-emission vehicles using the GHK simulator and Bayes estimates of a multinomial probit model," ZEW Discussion Papers 12-017, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
    3. Smith, Martin D., 2005. "State dependence and heterogeneity in fishing location choice," Journal of Environmental Economics and Management, Elsevier, vol. 50(2), pages 319-340, September.
    4. Achtnicht, Martin & Bühler, Georg & Hermeling, Claudia, 2012. "Impact of service station networks on purchase decisions of alternative-fuel vehicles," ZEW Discussion Papers 08-088 [rev.], ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
    5. Bhat, Chandra R. & Castelar, Saul, 2002. "A unified mixed logit framework for modeling revealed and stated preferences: formulation and application to congestion pricing analysis in the San Francisco Bay area," Transportation Research Part B: Methodological, Elsevier, vol. 36(7), pages 593-616, August.
    6. Hensher, David A., 2008. "Empirical approaches to combining revealed and stated preference data: Some recent developments with reference to urban mode choice," Research in Transportation Economics, Elsevier, vol. 23(1), pages 23-29, January.
    7. Brownstone, David & Train, Kenneth, 1998. "Forecasting new product penetration with flexible substitution patterns," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 109-129, November.
    8. Yetano Roche, María & Mourato, Susana & Fischedick, Manfred & Pietzner, Katja & Viebahn, Peter, 2010. "Public attitudes towards and demand for hydrogen and fuel cell vehicles: A review of the evidence and methodological implications," Energy Policy, Elsevier, vol. 38(10), pages 5301-5310, October.
    9. Brownstone, David & Bunch, David S & Train, Kenneth, 1999. "Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles," University of California Transportation Center, Working Papers qt45f996hh, University of California Transportation Center.
    10. Malte Schwoon, 2006. "Simulating the adoption of fuel cell vehicles," Journal of Evolutionary Economics, Springer, vol. 16(4), pages 435-472, October.
    11. Zhang, Yong & Yu, Yifeng & Zou, Bai, 2011. "Analyzing public awareness and acceptance of alternative fuel vehicles in China: The case of EV," Energy Policy, Elsevier, vol. 39(11), pages 7015-7024.
    12. Ye Feng & Don Fullerton & Li Gan, 2005. "Vehicle Choices, Miles Driven, and Pollution Policies," NBER Working Papers 11553, National Bureau of Economic Research, Inc.
    13. Teisl, Mario F. & Rubin, Jonathan & Noblet, Caroline L., 2008. "Non-dirty dancing? Interactions between eco-labels and consumers," Journal of Economic Psychology, Elsevier, vol. 29(2), pages 140-159, April.
    14. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    15. Achtnicht, Martin, 2012. "German car buyers' willingness to pay to reduce CO2 emissions," ZEW Discussion Papers 09-058 [rev.], ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
    16. Bhat, Chandra R. & Sen, Sudeshna & Eluru, Naveen, 2009. "The impact of demographics, built environment attributes, vehicle characteristics, and gasoline prices on household vehicle holdings and use," Transportation Research Part B: Methodological, Elsevier, vol. 43(1), pages 1-18, January.
    17. Alexandros Dimitropoulos & Piet Rietveld & Jos N. van Ommeren, 2011. "Consumer Valuation of Driving Range: A Meta-Analysis," Tinbergen Institute Discussion Papers 11-133/3, Tinbergen Institute.
    18. Golob, Thomas F. & Gould, Jane, 1998. "Projecting use of electric vehicles from household vehicle trials," Transportation Research Part B: Methodological, Elsevier, vol. 32(7), pages 441-454, September.
    19. Lee, Jongsu & Cho, Youngsang, 2009. "Demand forecasting of diesel passenger car considering consumer preference and government regulation in South Korea," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(4), pages 420-429, May.

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