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SUN Lu, YU Ye, GU Wen-jun. Car ownership prediction method based on principal component analysis and hidden Markov model[J]. Journal of Traffic and Transportation Engineering, 2013, 13(2): 92-98. doi: 10.19818/j.cnki.1671-1637.2013.02.014
Citation: SUN Lu, YU Ye, GU Wen-jun. Car ownership prediction method based on principal component analysis and hidden Markov model[J]. Journal of Traffic and Transportation Engineering, 2013, 13(2): 92-98. doi: 10.19818/j.cnki.1671-1637.2013.02.014
shu

Car ownership prediction method based on principal component analysis and hidden Markov model

doi: 10.19818/j.cnki.1671-1637.2013.02.014
  • 1.

    School of Transportation, Southeast University, Nanjing 210096, Jiangsu, China

  • 2.

    Department of Civil Engineering, The Catholic University of America, Washington DC 20064, USA

More Information
  • Author Bio:

    SUN Lu (1972-), male, professor, PhD, +86-25-83792619, sunl@cua.edu

  • Received Date: 2012-09-17
  • Publish Date: 2013-04-25
    Abstract
  • The usual prediction methods of car ownership were analyzed, a new car ownership prediction method based on principal component analysis(PCA) and hidden Markov model(HMM) was put out. The 11 indexes including gross national income, per capita GDP, total population number, urbanization rate, total fixed asset investment, gross import and export, urban resident disposable income, steel output, highway passenger transport volume, highway freight transport volume, total retail sales of consumer goods were taken as the main influence factors of car ownership, and PCA was used to extract the principal components of main influence factors. The principal component and car ownership were taken as independent variable and dependent variable respectively, and the regression analysis model was set up. The annual growth rates of regression prediction values for car ownership were taken as hidden state, the relative errors between regression prediction values and actual values were taken as visible signal, the hidden Markov model was built, and the regression prediction values of car ownership were modified.Analysis result shows that based on car ownerships and the historical data of main influence factors in 1994-2008, the numbers of modified car ownership in 2009 and 2010 are 6.220 96×107 and 7.825 12×107 by using the proposed method.Compared with the actual values of car ownership in 2009 and 2010, relative errors are-0.95% and 0.30% respectively. So car ownership prediction method based on PCA and HMM has a high prediction accuracy and is suitable for short-term prediction.

     

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