Trip Distribution
The following excerpt was taken from the Transportation Planning Handbook
published in 1992 by the Institute of Transportation Engineers (pp. 112-114).
Trip distribution models connect the trip origins and destination estimated by the trip
generation models to create estimated trips. Different trip distribution models are
developed for each of the trip purposes for which trip generation has been estimated. The
trip distribution models found most often in practice today are "gravity
models," so named because of their basis in Newtons law. . . .
The measure of separation between zones most commonly used for trip distribution is
roadway travel time, calculated from the computerized transportation networks. Most
transportation planning efforts use peak-period travel times as a measure of zonal
separation for home-based work and home-based school models. . . . Recent studies have
tried to incorporate travel cost and transit travel time into the separation measure. Cost
has been considered in an attempt to estimate effects on trip distribution of parking
costs, vehicle operating costs, and tolls.
Logit Model
Other trip distribution models that have been used include "opportunity"
models and logit models, both of which estimate the probability that travelers will accept
various destination options available. The logit formulation has recently been used for
the Portland, Oregon metropolitan area. As shown in Figure 4.20, the probability of
selecting a particular destination zone is based on the number of trip attractions
estimated for that destination zone relative to the total attractions in all possible
destination zones. The probability is applied to trip productions estimated for the origin
zone, making it conceptually similar to the gravity model.
Gravity model
Those models generally estimate the distribution of trips to be proportional to the
number of trip ends estimated by the trip generation models and inversely proportional to
a measure of separation between the origin and destination zones. The gravity model has
achieved virtually universal use because of its simplicity, its accuracy and due to its
support from the U.S. Department of Transportation. . . .
Developing a gravity model is a trial-and-error process that requires considerable
care. This process, often called calibration, identifies the appropriate decay function or
"friction factor", that represents the reluctance or impedance of persons to
make trips of various durations or distances. . . . The adjustments are made incrementally
with successive iterations of the model until the trip length frequency distribution
produced by the model closely matches the frequency distribution from the travel survey or
demonstrates an acceptable shape and average trip length.
An important consideration in developing trip distribution models is
"balancing" productions and attractions. One aspect of balance is to assure that
the total productions equal the total attractions in the study area for each trip purpose.
Deciding whether the productions or attractions should be the control total depends on
whether there is greater confidence in the production (usually population) growth estimate
or the attraction (usually employment) growth estimate. It is not unreasonable to average
the two (production and attraction) trip estimates. The productions and/or attractions for
all zones must then be factored so that their sum matches the control total. . . .
(p. 114) At each iteration of the gravity model, the total trips attracted to each zone
is adjusted so that the next iteration of the gravity model will send more or fewer trips
to that attraction zone, depending on whether the immediately previous total trips
attracted to that zone was lower or higher, respectively, than the trip attractions
estimated by the trip generation model. . . . Any unacceptable difference between the
generation and distribution model estimates after five iterations of the gravity model
usually indicates an inconsistency in the assumptions or functions of the trip
distribution model and the growth allocation model.
One other consideration in developing a trip distribution model is how to handle
unexplained and unacceptable differences between observed and estimated travel patterns.
Rather than conduct extensive research to try to find an explanation for all such
phenomena, the accepted practical approach is to factor the model estimates to match
observed patterns. . . . With the gravity model, and often with other models in this
situation, the adjustment factors are called "K" factors. The "K"
factors are developed for individual trip interchanges and are assigned values that adjust
the estimated trips for the interchanges of concern to match the observed values.
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