Table: Trip Destinations per unit (Acre) of Land Ring Manufacturing Commercial Open Space etc

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were arrayed and interpreted on a distance-from-CBD scale. For example, commercial land
use in ring 0 (the CBD and vicinity) was found to generate 728 vehicle trips per day in 1956.
That same land use in ring 5 (about 17 km (11 mi) from the CBD) generated about 150 trips
per day.
The case of trip destinations will illustrate use of the concept of activity decline with intensity
(as measured by distance from CBD) worked. Destination data are arrayed:
Table: Trip Destinations per unit (Acre) of Land
Ring Manufacturing Commercial Open Space etc.
0
x
1m
x
1c
x
1os
x
1n
7
x
7m
x
7c
x
7os
x
7n
The land use analysis provides information on how land uses will change from an initial year
(say t = 0) to some forecast year (say t = 20). Suppose we are examining a zone. We take the
mix of land uses projected, say, for year t = 20 and apply the trip destination rates for the ring
in which the zone is located. That is, there will this many acres of commercial land use, that
many acres of public open space, etc., in the zone. The acres of each use type are multiplied
by the ring specific destination rates. The result is summed to yield the zone’s trip
destinations. The CATS assumed that trip destination rates would not change over time.
Revisions to the analysis
As was true for land use analysis, the approach developed at CATS was considerably
modified in later studies. The conventional four-step paradigm evolved as follows: Types of
trips are considered. Home-based (residential) trips are divided into work and other, with
major attention given to work trips. Movement associated with the home end of a trip is
called trip production, whether the trip is leaving or coming to the home. Non-home-based or
non-residential trips are those a home base is not involved. In this case, the term production
is given to the origin of a trip and the term attraction refers to the destination of the trip.
Residential trip generation analysis is often undertaken using 
statistical regression
. Person,
transit, walking, and auto trips per unit of time are regressed on variables thought to be
explanatory, such as: household size, number of workers in the household, persons in an age

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group, type of residence (single family, apartment, etc.), and so on. Usually, measures on five
to seven independent variables are available; additive causality is assumed.
Regressions are also made at the aggregate/zone level. Variability among households within
a zone isn’t measured when data are aggregated. High 
correlation coefficients
are found
when regressions are run on aggregate data, about 0.90, but lower coefficients, about 0.25,
are found when regressions are made on observation units such as households. In short,
there is much variability that is hidden by aggregation.
Sometimes 
cross-classification
techniques are applied to residential trip generation
problems. The CATS procedure described above is a cross-classification procedure.
Classification techniques are often used for non-residential trip generation. First, the type of
land use is a factor influencing travel, it is regarded as a causal factor. A list of land uses and
associated trip rates illustrated a simple version of the use of this technique: