The report discusses conventional forecasting methods and the existing GLC technique (which uses a category analysis procedure), and describes the proposed technique in which conventional techniques are reconsidered and the distribution of all households which are at a given level of car ownership are examined. With this technique it is possible to obtain a series of income distributions, one for each category of car ownership, for which may be derived the functions/the probabilities Pn of having n cars. It is noted that some properties of the gamma distribution (a 2 parameter distribution; the 2 parameters control the scale and the spread of the distribution independently) can be used which enable all the relationships to be described in one single consistent framework of related gamma distributions: each household income distribution for a given car ownership level is gamma with a common scale factor for all car ownership levels; the proportion of households with a given number of cars is given by a discrete gamma distribution; the relationships described in such techniques are logarithmic logistics. This method enables better use of data in the calibration as the three relationships have to interlock. Estimations based on U.K. and U.S. data are presented, and the variation of parameters in the analysis of various sets of data is discussed.

  • Supplemental Notes:
    • This paper appears in "Urban Traffic Models", which is a publication containing the Proceedings of Seminar N of the Summer Annual Meeting at University Warwick, England during July, 1975.
  • Corporate Authors:

    Planning and Transport Res and Computation Co Ltd

    167 Oxford Street
    London W1R 1AH,   England 
  • Authors:
    • Mogridge, MJH
  • Publication Date: 1975-7

Media Info

  • Features: Figures; References; Tables;
  • Pagination: p. 200-213
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 00148097
  • Record Type: Publication
  • Report/Paper Numbers: P122
  • Files: TRIS
  • Created Date: Feb 23 1977 12:00AM