M. D. ANDERSON ET AL. 189

and the number of trucks passing through the community.

Parameters in the gravity model were set to constrain the

E-I and I-E truck numbers such that the total number of

trucks at the external stations did not exceed boundary

conditions. A separate gravity model, which used the

modeling details for the City of Huntsville, was used for

the internal truck trips that included a reduction factor to

limit the number of trips. As before, mode split was not

included in the model and truck trips were assigned to

the Huntsville network without passenger cars to allow

truck access to the major roadways.

A scatter plot was drawn to compare actual truck count

and the trucks assigned from the model for each per-

centage split. A scatter plot for the 80% E-I and I-E with

20% internal trips is shown in Figure 6. As can be seen,

the results appear to align much closer to the 1:1 slope

with the trip distribution adjustment.

For comparison, the Nash-Sutcliffe efficiency coeffi-

cient was calculated for each trip distribution split. The

results were as follow:

NS Coefficient = 0.59 for the 90% (E-I and I-E) and

10% (I-I),

NS Coefficient = 0.61 for the 80% (E-I and I-E) and

20% (I-I),

NS Coefficient = 0.62 for the 70% (E-I and I-E) and

30% (I-I), and

NS Coefficient = 0.61 for the 60% percent (E-I and

I-E) and 40% (I-I).

As these results show, there is little difference between

the models. However, all the models show significant

improvements over the 100% internal distribution.

Further statistical tests were performed to determine if

the data obtained from the travel demand model were

similar to actual truck counts. MINITAB™ Statistical

Software was used to analyze the data and to perform the

analysis of variance (ANOVA) test. The results show no

statistical evidence that actual truck volumes are different

from the volumes assigned by the model. Further, using

Figure 6. Scatter plot of truck traffic with distribution mo-

dification.

the Mann-Whitney non-parametric test, it is found that

that the QRFM data likely come from the same popula-

tion as the actual data.

The implementation of the methodology would require

that smaller communities develop a truck trip purpose

using the QRFM equations. However, the resulting truck

values need to be converted into internal truck trips and

internal-external and external-internal truck trips based

on the percent trips expected to leave the study area.

Then, the two purposes need to be included into the

modeling process as would be followed using any num-

ber of trip purposes.

5. Statistical Analysis

This paper demonstrated that trip generation equations

from the QRFM, when calculated using socio-economic

data from a medium sized travel demand model, can ac-

curately reflect the locations where truck trips are likely

to originate or terminate inside a community. Secondar-

ily, this paper showed that the use of an appropriate trip

distribution method that accounts for freight movements

entering and leaving the study area produces an accurate

forecast of trucks on existing roadway infrastructure, the

percent values to use will be based on varying the data

and determining the best fit or using the recommended

values presented in this paper. This ability to successfully

model freight in an urban area can be used to overcome

the limitation of neglecting freight in travel demand

modeling processes.

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