Journal of Transportation Technologies, 2013, 3, 185-189 Published Online July 2013 (
Effectively Using the QRFM to Model Truck Trips in
Medium-Sized Urban Communities
Michael D. Anderson1, Mary C. Dondapati1, Gregory A. Harris2
1Department of Civil and Environmental Engineering, University of Alabama in Huntsville, Huntsville, USA
2Center for Management and Economic Research, University of Alabama in Huntsville, Huntsville, USA
Received March 21, 2013; revised April 22, 2013; accepted April 30, 2013
Copyright © 2013 Michael D. Anderson et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This paper analyses the effectiveness of applying the Quick Response Freight Manual (QRFM) to model freight trans-
portation. Typically, freight transportation is indirectly modeled or as an after-thought. Increasing freight volumes, cou-
pled with cost saving strategies such as just-in-time delivery systems, require that transportation policymakers analyze
infrastructure needs and make investment decisions that explicitly include freight volumes as a component. This paper
contains a case study using a medium sized urban area travel model and the QRFM trip generation and a distribution
methodology to provide a framework for freight planning that can be used to improve resource allocation decisions.
Keywords: Freight Modeling; Travel Demand Modeling
1. Introduction
The efficient and effective movement of freight is a
critical component in the transformation and growth of
the economy. Often, transportation planners use urban
transportation planning models, which are representa-
tions of the existing transportation infrastructure to de-
termine the impacts of future changes. These planning
models are developed and validated to reflect existing
traffic volumes and patterns. After validation, the models
are used for forecasting daily traffic volumes on primary
arterials and freeways and evaluate changes in roadway
infrastructure and socio-economic characteristics. In
small and medium sized urban communities, proper
roadway infrastructure resource allocation decisions
based on data obtained from the community’s travel de-
mand model and long-range transportation planning
process could potentially be the determining factor be-
tween continued community growth and stagnation.
Since the modeling process is important, it is critical
that the models used provide the best forecasts of future
conditions. Unfortunately, freight transportation require-
ments are often not included in travel demand models
developed and maintained in small communities, or else,
freight trips are included in these models through very
simplified methodologies.
This paper examines the potential to use available
freight trip generation factors and a distribution scheme
to determine freight transportation demand appropriate
for incorporation into a community travel demand model.
First, the paper presents background into travel demand
forecasting and the Quick Response Freight Manual
(QRFM) trip generation equations [1,2]. Next, the paper
applies the model through a case study of Huntsville, AL,
a medium-sized community in the north-central portion
of the state. A statistical analysis of the QRFM technique
is applied to the network using a variety of distribution
schemes to improve its forecasting ability. The paper
concludes that the proper incorporation of freight trans-
portation needs into the travel demand modeling process
can improve its results and should lead to improved in-
vestment decisions for the community.
2. Transportation Planning Background and
Freight Specifics
The background for this paper is the traditional four step
modeling process used in most small and medium sized
urban areas and specifics of the process that deal with
freight. The traditional transportation planning process
follows the sequential four-step methodology: trip gen-
eration, trip distribution, mode split, and traffic assign-
ment. The first step in the process, trip generation, uses
socio-economic data, aggregated to traffic analysis zones,
to determine the number of trips produced by and at-
tracted to each zone in the study area [3]. For passenger
opyright © 2013 SciRes. JTTs
transportation, factors that can influence trips produced
from or attracted to a zone are: household income and
size, automobile ownership, type of businesses, and trip
purpose [3]. The trip generation step then converts these
zonal data values into trip purposes. However, in most
small and medium sized urban communities, there is no
model developed for freight productions or attractions
since it is time consuming and costly to survey busi-
nesses and manufacturers on their specific freight re-
Trip distribution connects trip origins and destinations
for the development of a trip interchange matrix. The two
main factors considered are trip length and the travel
direction or orientation. The most common method used
for trip distribution is a gravity model, which is based on
Newton’s law [3]. The gravity model predicts that trip
interchanges between zones are directly proportional to
the productions and attractions in the zones and inversely
proportional to the spatial separation between zones [3].
In other words, zones with more activities or businesses
are more likely to exchange more trips, and zones with
longer distances between them are likely to exchange
fewer trips. For freight, it is expected that the trip distri-
bution would be similarly performed.
Modal split is used to estimate how many trips will use
public transit and how many trips will use private vehi-
cles, typically using a logit model [3]. However, this step
of the process is generally ignored in small and medium
sized communities, as transit ridership is not significant.
With freight however, this step would contrast truck
versus alternative modes of shipment (rail, water, and air)
and therefore it is significant. As limited availability for
alternate freight shipping models often exists in medium
sized communities, this step is still not included in the
modeling process.
Traffic from the modal split analysis is then assigned
to available roadways or transit routes using Waldrop’s
equilibrium theorem, or some approximation of equilib-
rium. By this theory, under equilibrium conditions traffic
arranges itself in congested networks in such a way that
no individual trip maker can reduce his path costs by
switching routes [3]. Regarding freight, it is not neces-
sarily logical to assume freight shipments will likely
change their route due to congestion effects, at least not
off the major roadways within the communities.
To overcome the absence of freight in transportation
models, the original Quick Response Freight Manual
(QRFM) and its updated version QRFM II, were pre-
pared for the Federal Highway Administration [1,2]. The
objective of the reports were to provide background in-
formation on the freight transportation system and factors
affecting freight demand to planners who may be rela-
tively new to the inclusion of freight planning and to
provide simple techniques and transferable parameters
that can be used to develop commercial vehicle trip ta-
bles which can then be merged with passenger vehicle
trip tables developed through the conventional four-step
planning process. The QRFM report identifies trip gen-
eration factors that define production and attraction val-
ues manageable within a small community. To support
trip distribution, the QRFM provides a series of friction
factors that can be incorporated into the gravity model to
specify the expected length of freight movements. Fig-
ure 1 provides the trip generation equations and Figure 2
the friction factor equations.
3. Case Study: Huntsville, AL
Huntsville, Alabama with a population of approximately
300,000 was selected as case study to analyze the incor-
poration of freight into the modeling process. For this
research, the data on the transportation network for the
City of Huntsville was acquired from the Huntsville
Metropolitan Planning Organization (MPO); see Figure
3 [4].
The research was performed by applying the trip gen-
eration rates obtained from the QRFM to the socio-eco-
nomic data collected by the Huntsville MPO. For each
zone, the socio-economic data were converted into
freight trips using the rates provided by the QRFM. A
Commercial Vehicle Trip Destinations (or Origins) per Unit per Day
Four-Tire Vehicles Single Unit Trucks (6 + Tires) Combinations TOTAL
Agriculture, Mining and Construction 1.110 0.289 0.174 1.573
Manufacturing, Transportation, Communications,
Utilities and Wholesale Trade 0.938 0.242 0.104 1.284
Retail Trade 0.888 0.253 0.085 1.206
Office and Services 0.437 0.068 0.009 0.514
Households 0.251 0.099 0.028 0.388
Figure 1. Trip generation rates from the QRFM [2].
Copyright © 2013 SciRes. JTTs
Figure 2. Friction factors from the original QRFM. (Source
Cambridge Systematics, Inc. Quick Response Freight Man-
ual II. Federal Highway Administration. Publication No.
FHWA-HOP-08-010. September 2007).
Figure 3. Huntsville, AL planning model.
visual validation of the trip generation model results as
they related to the total non-retail employment in the
study city was performed by developing a thematic map
showing the amount of non-retail employment within
each traffic analysis zone overlaid with a dot density plot
of the freight trips (see Figure 4). The figure indicated
that the QRFM freight trips were located in areas of
higher non-retail employment. This result was expected
and validated the use of the QRFM model in the planning
The Huntsville model was based on trip generation,
distribution and assignment. Common with the practice
in planning studies this model used the static traffic as-
signment technique. The rationale is that it mirrored the
current modeling system used in the community and ap-
proved by the state for transportation forecasting proc-
esses. This ensured that the model would be accepted
Figure 4. Freight trips versus non-retail employment.
upon development of a successful application. Though
not used, the analysis could have benefitted by employ-
ing dynamic traffic assignment techniques, such as those
available in PARAMICS, VISSUM or VISTA, that have
the capability to move vehicles through the network us-
ing car following and lane changing models [5].
4. Statistical Analysis
An analysis of the model for calculating truck trips was
performed by developing freight trip purposes and de-
signing a series of travel modules to perform trip distri-
bution plus assigning freight trips to roadways in the
network. Initially, the trips produced and attracted were
distributed using a gravity model that treated truck trips
similar to how passenger trips are treated by distributing
freight trips to zones within the study area. Truck counts
at external stations in the model were included as a sepa-
rate trip purpose and distributed between these stations.
For traffic assignment, the freight trips were assigned to
the network without the presence of passenger cars using
the shortest path algorithm where all trucks were as-
sumed to take the shortest travel time path through a
network. This algorithm limits the number of trucks as-
signed to local roadways due to the slow travel speeds on
these roadways, a result that could also have been ob-
tained with an impedance function. Though the possibil-
Copyright © 2013 SciRes. JTTs
ity exists for some trucks to be assigned to local road-
ways, the number of such trucks is assumed to be mini-
The accuracy of the assignment of truck volumes was
determined by comparing the assignment results to the
actual truck volumes reported by Alabama Department of
Transportation (ALDOT). The first comparison used a
scatter plot of actual truck traffic volume versus traffic
volumes from the QRFM. That plot in Figure 5 shows
that there is no clear relationship between the QRFM
results and the actual freight counts in Huntsville using
the 100% internal distribution.
To statistically measure the difference between the as-
signed truck traffic and actual truck counts, the Nash-
Sutcliffe (NS) coefficient was calculated [6]. The value
of this coefficient ranges from −∞ to 1, with a coefficient
of one (E = 1) showing a perfect match of forecasted
counts to the ground counts. A zero coefficient (E = 0)
shows that the forecasted values are as accurate as the
mean of the ground counts, whereas a coefficient less
than zero (−∞ < E < 0) occurs when the forecasted mean
is less than the ground counts. In other words, this coef-
ficient gives a measure of scatter variation from the 1:1
slope line of modeled truck counts versus the ground
counts. The more deviation of points from the 1:1 slope
line, the lower the coefficient. The greater the NS-value
the better is the forecast. This coefficient can be calcu-
lated using the formula:
 
NS1Modeled CountsGround CountsGround CountsMean Ground Counts 
The application of the Nash-Sutcliffe test to the data
gave an efficiency coefficient of 1.45 showing that tak-
ing an average value of the truck counts from ALDOT
would better predict truck flows than the travel demand
Further statistical tests were performed to determine
whether the data obtained from the travel demand model
were similar to actual truck counts. The MINITAB™
statistical software was used to conduct the analysis of
variance (ANOVA) test. The results provide statistical
evidence to suggest that actual truck volumes are differ-
ent from the volumes assigned by the model.
To improve the results, an alternate trip distribution
method was employed. This method was developed from
the results of a study being done in Mobile, Alabama [7].
The flow patterns collected from the Mobile area in Ta-
ble 1 show that external-internal (E-I) truck trips and
internal-external (I-E) truck trips represent over 80% of
Model Vol umes Ver sus Truck Counts (100%
Internal Trips)
05001000 1500 2000 25003000 3500
Truck Count
Model Assignm ent
Figure 5. Scatter plot of truck traffic.
the total truck volumes, while the internal-internal (I-I)
truck trips account for less than 20%. This implies that
approximately 80% of the raw materials for manufactur-
ing are generated outside the area, and approximately
80% of the finished products are exported to points out-
side the area.
To account for changes in truck distribution in the
model, the modules used to run the Huntsville MPO
travel demand model were adjusted to account for freight
trips distributed into the community from outside (E-I),
and outward from the community to points beyond the
study area (I-E). An experiment was designed to include
four different trip distribution levels:
90% (E-I and I-E) and 10% (I-I),
80% (E-I and I-E) and 20% (I-I),
70% (E-I and I-E) and 30% (I-I), and
60% (E-I and I-E) and 40% (I-I).
The reason for not simply using the 80% E-I and I-E
found in the Mobile project is the uncertainty that Hunts-
ville would perform similarly as Mobile due to socio-
economic differences in the two communities and the
influence of the Port of Mobile.
The E-I and I-E truck trip distributions were developed
using the total number of trucks crossing the study area
boundary. The total number of trucks at the boundaries
was split by percentage into the number of trucks ex-
pected to enter and leave the community (E-I and I-E)
Table 1. Freight locations for mobile area.
Freight Origin/Destination
Location Origins Destinations
Within Mobile County 14.5% 16.4%
Outside Mobile County 84.5% 80.7%
Local Port 1.0% 2.8%
Copyright © 2013 SciRes. JTTs
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-
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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