The coefficients a 0 toan which occur within typical trip generation models as
shown in equation 2.1 are determined through regression analysis. Manual solu-
tions from multiple regression coefficients can be tedious and time-consuming
but software packages are readily available for solving them. For a given trip
generation equation, the coefficients can be assumed to remain constant over
time for a given specified geographical location with uniform demographic and
socio-economic factors.
In developing such regression equations, among the main assumptions made
is that all the variables on the right-hand side of the equation are independent
of each other. It may not, however, be possible for the transportation expert to
conform to such a requirement and this may leave the procedure open to a
certain level of criticism. In addition, basic errors in the regression equation may
exist as a result of biases or inaccuracies in the survey data from which it was
derived. Equation 2.1 assumes that the regression of the dependent variable on
the independent variables is linear, whereas in reality this may not be the case.
Difficulties with the use of regression analysis for the analysis of trip genera-
tions have resulted in support for the use of models with the person or, more
often, the household, at its basis. This process of estimating trip generations
directly from household data is known as category analysis. Within it, house-
holds are subdivided into smaller groupings that are known to possess set trip-
making patterns. Category analysis assumes that the volume of trips generated
depend on the characteristics of households together with their location rela-
tive to places of work. These characteristics are easily measured. They include
household income, car ownership, family size, number of workers within the
household and housing density. The method does, however, assume that both
car ownership and real income levels will increase in the future. This may not
necessarily be the case.
For example, the more people within a household and the more cars available
to them, the more trips they will make; say we define 15 subgroups in terms of
two characteristics – numbers within the household and number of cars avail-
able – and we estimate the number of trips each subgroup is likely to make
during the course of the day. An example of category analysis figures is given
in Table 2.1.
Forecasting Future Traffic Flows 21Available cars per household
Household pop. 0 1 2 +
1 1.04 1.85 2.15
2 2.02 3.10 3.80
3 2.60 3.40 4.00
4 3.80 4.80 6.40
5 + 4.20 5.20 6.40Table 2.1Category
analysis table (daily trip
rates per household
category)