Projections of Household Vehicle Consumption in the United States

  • Yi Zeng
  • Kenneth C. Land
  • Danan Gu
  • Zhenglian Wang
Chapter
Part of the The Springer Series on Demographic Methods and Population Analysis book series (PSDE, volume 36)

Abstract

Forecasts of household vehicle consumption are important for automobile market analyses and related socioeconomic planning. This chapter employs the ProFamy extended cohort-component method to project household vehicle consumption from 2000 to 2025 across four regions of the United States (the Northeast, Midwest, South, and West). The results show that the total number of household vehicles (The term “household vehicles” refers to vehicles for household use in this book) in 2025 will reach 235 million, representing a 31 % increase over 25 years. About a half of the increase is due to the consumption of cars, while the household consumption of vans will increase at a faster rate than that of cars and trucks. Household vehicle consumption will grow more in white non-Hispanic and Hispanic households in comparison with black non-Hispanic and Asian and other non-Hispanic households. Owners of household vehicles in the United States will be aging quickly. Among households of different sizes, the largest increase in household vehicles will come from two-person households. Across the four regions, the largest increase in household vehicle consumption will be in the South, followed by the West, Midwest, and Northeast.

Keywords

Ownership Rate Cumulative Increase National Household Travel Survey Household Vehicle Household Income Category 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Bhat, C. R., & Sen, S. (2005). Household vehicle type holdings and usage: An application of the multiple discrete-continuous extreme value (MDCEV) model. Transportation Research, 40(B), 35–53.Google Scholar
  2. Buettner, T., & Grubler, A. (1995). The birth of a ‘green’ generation? Generational dynamics of consumption patterns. Technological Forecasting and Social Change, 50, 113–134.CrossRefGoogle Scholar
  3. California Energy Commission. (2003). Forecasts of California transportation energy demand, 2003–2023. Prepared in support of the transportation report under the integrated energy policy report proceeding, Docket#02-IEP-01.Google Scholar
  4. Cao, X., & Mokhtarian, P. L. (2003). The future demand for alternative fuel passenger vehicles: A diffusion of innovation approach. http://aqp.engr.ucdavis.edu/Documents/AFV%20SCENARIO_June. pdf. Accessed 10 June 2010.
  5. Dalton, M., O’Neill, B., Prskawetz, A., Jiang, L., & Pitkin, J. (2008). Population aging and future carbon emissions in the United States. Energy Economics, 30, 642–675.CrossRefGoogle Scholar
  6. Ediev, D. (2007). On projecting the distribution of private households by size. Vienna Institute of Demography Working paper, 4/2007.Google Scholar
  7. Ediev, D., Yavuz, S., & Yüceşahin, M. (2012). Private households in Turkey: Big changes ahead. Population Review, 51(1), 28–49.Google Scholar
  8. Gu, D. (2008). General data assessment of the Chinese longitudinal healthy longevity survey in 2002. In Y. Zeng, D. L. Poston, D. A. Vlosky, & D. Gu (Eds.), Healthy longevity in China demographic socioeconomic and psychological dimensions (pp. 39–59). Dordrecht: Springer.CrossRefGoogle Scholar
  9. Jiang, F. (2012). Report: Nearly 90 percent of Chinese families own houses. People’s Daily Online. 15 May 2012. http://english.peopledaily.com.cn/90882/7817224.html. [in Chinese]. Accessed 31 Mar 2013.
  10. Morgan, S. P. (2004). Interstate differentials in demographic rates are mostly caused by differences in racial compositions. Personal e-mail communication.Google Scholar
  11. Myers, D., Pitkin, J., & Park, J. (2002). Estimation of housing needs amid population growth and change. Housing Policy Debate, 13(3), 567–596.CrossRefGoogle Scholar
  12. National Center for Health Statistics (NCHS). (2012). Health, United States, 2011: List of trend tables. http://www.cdc.gov/nchs/hus/contents2011.htm. Accessed 16 Nov 2012.
  13. OECD Statistics. (2007). Pensions at a glance 2007: Highlights. OECD.Google Scholar
  14. Paget, W. J., & Timaeus, I. M. (1994). A relational Gompertz model of male fertility. Development and assessment. Population Studies, 48, 333–340.CrossRefGoogle Scholar
  15. Rogers, A. (1975). Introduction to multiregional mathematical demography. New York: Wiley.Google Scholar
  16. Spicer, K., Diamond, I., & Bhrolcham, M. N. (1992). Into the twenty-first century with British households. International Journal of Forecasting, 8, 529–539.CrossRefGoogle Scholar
  17. Stupp, P. W. (1988). A general procedure for estimating intercensal age schedules. Population Index, 54, 209–234.CrossRefGoogle Scholar
  18. Suthers, K., Kim, J. K., & Crimmins, E. M. (2003). Life expectancy with cognitive impairment in the older population of the United States. Journal of Gerontology: Social Sciences, 58B(3), S179–S186.Google Scholar
  19. U.S. Census Bureau. (2012). The 2012 statistical abstract. Table 1337. Single-parent households. http://www.census.gov/compendia/statab/cats/international_statistics/population_households.html. Accessed 16 Nov 2012.
  20. Vance, C., & Buchheim, S. (2004). Household demographic composition, community design and travel behavior: An analysis of motor vehicle use in Germany. Paper prepared for presentation at the Research on Women’s Issues in Transportation Conference, Chicago.Google Scholar
  21. Vlasic, B., & Bunkley, N. (2008). Hazardous conditions for the auto industry. New York Times. http://www.nytimes.com/2008/10/02/business/02sales.html?_r=1&partner=rssnyt&emc=rss. Accessed 10 June 2010.
  22. Zeng, Y., Land, K. C., Wang, Z., & Gu, D. (2013a). Household and living arrangements projections at the sub-national level: An extended cohort-component approach. Demography, 50, 827–852.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Yi Zeng
    • 1
    • 2
  • Kenneth C. Land
    • 3
  • Danan Gu
    • 4
  • Zhenglian Wang
    • 5
    • 6
  1. 1.Center for Study of Aging and Human Development Medical SchoolDuke UniversityDurhamUSA
  2. 2.National School of Development Center for Healthy Aging and Development StudiesPeking UniversityBeijingChina
  3. 3.Department of Sociology and Center for Population Health and Aging Population Research InstituteDuke UniversityDurhamUSA
  4. 4.Population DivisionUnited NationsNew YorkUSA
  5. 5.Center for Population Health and Aging Population Research InstituteDuke UniversityDurhamUSA
  6. 6.Household and Consumption Forecasting, Inc.Chapel HillUSA

Personalised recommendations