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Assignment modeled flows and the target flows. For example, the lack of
intracounty traffic being assigned by the model to the road-
The daily truck trip table was assigned to the highway ways will consistently give low estimates because the traffic
network using a FORTRAN program that used an "all or count data includes these flows. The overall model explained
nothing" assignment procedure based on the travel time 48% of the variation in total commercial traffic using the
between zones. Based on initial results, adjusted speeds were flows assigned at 40 rural locations.
developed based on the following formula to account for
the over-assignment of vehicles to interstate links compared
RAIL ASSIGNMENT
to other roadways:
New Speed = Old Speed + (2 × (65-Old Speed)) No route segment-specific data on rail flows was available to
which the assigned values could be compared. A visual exami-
Rail assignment procedures were somewhat different nation of rail flows was made to assess their reasonableness.
because rail carriers tend to consider the use of mainline
trackage as an equal or more important variable than the
Model Application
directness of the route. For this reason, a new "cost of move-
ment" variable was developed for rail that incorporated a The Indiana Commodity Transport Model has not been
distance minimizing component as well as a component applied to date, although the 1998 year trip tables crated by
related to the magnitude of volume of the rail-line. This the model are being used as the basis for the development of
measure lessens the length of line segments by dividing the InDOT's freight truck trip table in an update of the Indiana
segment by its traffic density and takes the form: Statewide Travel Demand Model, now under development.
I = (L(1/(D + 1)))
Performance Measures and Evaluation
where
I = the index of spatial separation; No performance measures were developed for this research
L = the length of the line segment of the network; and, model.
D = the traffic density of the line in millions of gross ton-
miles per year.
8.9 Case Study Florida Intermodal
Statewide Highway Freight
Model Validation Model (FISHFM)
Trip Generation
Background
Context
No data was available to validate the trip generation model.
In 2001, the State of Florida had a gross state product of
nearly $500 billion, or 5% of the gross domestic product of
Trip Distribution
the United States.23 If Florida were a separate country, its
No data was available to validate the trip distribution economy would be the 12th largest in the world, larger than
model. that of India, South Korea, Netherlands, and Australia.24 The
U.S. Census Bureau's CFS shows that in 1997 $214 billion of
goods shipments representing 397 million tons originated in
Mode Choice Florida. The CFS also indicates that of those shipments 73%
No data was available to validate the mode choice model. by value and 78% by weight moved by truck. In 1997,
Florida's seaports and airports handled $64 billion of exports
and imports, with trucks the predominant mode of transport
Modal Assignment to and from these facilities.25 A study by Cambridge System-
atics, Inc. for the Florida Chamber of Commerce, Transporta-
TRUCK ASSIGNMENT
tion Cornerstone Florida, concluded that the key to the state's
The 21 categories of goods were aggregated to create total economic growth and competitiveness is an efficient inter-
flow trip tables assigned to the roadway network using an all modal transportation system. Transportation costs, including
or nothing assignment procedure. The resulting truck vol- trucking, currently constitute 5% of the price of goods both
umes were then compared against actual traffic count data on nationally and in Florida.
Indiana's highways from 1991 to 1994. Adjustments were The Florida Department of Transportation (FDOT), recog-
made to account for inherent inconsistencies between the nizing the importance of intermodal freight in the state's econ-
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omy, has advanced the freight planning process by sponsoring Many truck trips in Florida begin or end at intermodal ter-
the Florida Freight Stakeholders Task Force and initiating a minals, either as long-distance movements or as short-haul
Strategic Intermodal System (SIS) Plan. A map of the SIS is connections between intermodal terminals. Because rail, air,
shown in Figure 8.16. Transportation Cornerstone Florida calls and water serve as important components of the freight sys-
for focused investment on trade corridors and international tem, the model determines how freight traffic is allocated and
gateways and greater attention to freight mobility and eco- routed among all freight modes in order to produce truck
nomic development in the planning process. forecasts. While a primary purpose of the model is to forecast
truck volumes on highways, the data and forecasts of other
Objective and Purpose of the Model freight modes are important as well.
FISHFM was designed to support the project-related work
of FDOT and Florida's metropolitan planning organizations, General Approach
which are required by Federal law to consider factors of
Model Class
freight mobility. The purpose of the model was to identify
deficiencies and needs and to test solutions on major freight The FISHFM is a four-step commodity forecasting model.
corridors throughout the state. These freight corridors suffer Florida has a statewide highway model in which total truck
from considerable congestion as they pass through metropol- trips are forecasted based on total employment and are
itan areas. For example, I-95 in South Florida is not only a assigned together with auto trips. An existing four-step model
major international freight corridor, it is also the main thor- for passenger auto and total truck traffic provided the state
oughfare for local travel in major metropolitan areas, includ- zone structure, highway network, and employment data that
ing Miami, Daytona and Jacksonville. I-4 in Central Florida served as the structure for developing the commodity model.
is heavily used by both truckers and tourists and is the site of The four-step commodity forecasting model is described in
a growing high-technology industry. In addition, the local detail in Section 6.4.
highway connections between major freight corridors and
intermodal terminals --warehouses, seaports, and airports--
are often the weakest link in the intermodal highway chain. Modes
The truck freight model will be integrated with MPO trans-
Even though the primary purpose of the FISHFM was
portation models to ensure that needs and deficiencies at the
to analyze freight truck traffic, the model development rec-
local level that impact efficient freight transportation can eas-
ognized that over 80% of the freight by tonnage serving
ily be identified.
Florida's major commercial airports, deepwater ports, and
rail container terminals is transported by truck. These inter-
modal facilities generate significant truck volumes at concen-
trated locations. The model development further recognized
that the rail, water, and air freight systems are important
competitors to truck freight. Understanding the demands of
other modes was deemed a critical component of the model
development.
A primary purpose of FISHFM was to forecast truck volumes
on highways. However, the data and forecasts of other freight
modes also were determined to be valuable as FDOT prepares
to implement a Statewide Intermodal Systems Plan and re-
sponds to its Transportation Land Use Study Committee's
recommendation that the Florida Intrastate Highway System
(FIHS) be expanded to a Florida Intermodal Transportation
System (FITS) covering all modes.
Markets
Trucking in Florida consists of very different markets:
Source: Strategic Intermodal System Plan, Florida Department of Transportation, long-haul interstate/international, intrastate, private/for-
April 2004.
hire, truckload/less-than-truckload, local/metropolitan de-
Figure 8.16. Florida's Strategic Intermodal System. livery, and drayage (truck shipment between ports, airports,
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and rail terminals). These markets have different needs, use demand estimation model. Base year values for these data
different vehicles (combination vehicles versus panel trucks) are used to calibrate the trip generation (production and at-
and are sensitive to different variables. Based on the data traction) equations. Forecast values for these data are then
available to support the development of the model and the used in the generation (production and attraction) equa-
role of MPOs in planning for local/metropolitan delivery, the tions to predict the number of freight trips that will be gen-
markets selected for inclusion in FISHFM were interregional erated in future years.
freight shipments within Florida, drayage movement to and Population serves as an input variable in the trip genera-
from intermodal terminals, and interstate freight shipments tion (attraction) equations. Population is one of the key
of all kinds. In order to properly account for the various char- variables that determine regionwide consumption of goods
acteristics influencing the interstate shipment of freight, the originating from other areas of Florida and nationwide. Base
model had to cover all of North America, although at a level year data were collected from the U.S. Census Bureau's 1998
of zone and network detail more geographically aggregated U.S. Census of population, Florida MPOs, local planning de-
than that for Florida alone. partments, and FSUTMS data (ZDATA1) sets. Future year
data were forecast from Florida's Long-Term Economic
Framework Forecast, Florida Population Studies-population projections
for Florida counties, MPO forecasts, and FSUTMS data
Florida's Model Task Force decided that the structure of (ZDATA1) forecasts.
the FISHFM should follow the basic framework of the four- Employment by commodity sector serves as an independ-
step Florida Standard Urban Transportation Model Structure ent variable in trip generation (production and attraction)
(FSUTMS) passenger process. This requires that tons of com- equations for freight tonnage produced and attracted by
modities be generated and distributed and that a mode split commodity group. Employment data by industry code are
component be used to determine which tons are shipped by the principal explanatory variables in the trip generation
truck and other modes. Truck trips identified in the mode equations. Base year data were collected from the Regional
split process then are assigned to the statewide highway net- Economic Information System (employment by standard
work. All model components operate as part of the FSUTMS industrial classification, or SIC), County Business Patterns
software. Following the FSTUMS approach results in a model (SIC employment by county), SIC employees by TAZ,
that is easily understood by users and ensures compatibility Florida MPOs, local planning departments, FSUTMS data
with FSUTMS and the statewide passenger model. (ZDATA2) sets, and the Florida Department of Labor. Future
year data were estimated using the Florida Long-Term
TRUCK TYPES Economic Forecast.
The FISHFM focuses primarily on long-distance commod-
ity freight movements. It captures large trucks moving on the FORECAST GROWTH OF EXTERNAL MARKETS
FIHS, the shipment of commodities between regions in While population and employment were chosen to be the
Florida, and the shipment of freight between Florida and the forecasting data for freight shipments to and from Florida
rest of North America. These truck trips currently represent TAZs, the data were not available or suitable to forecast freight
about 25% of the total truck trips in Florida, but 45% of the shipments for the zones located outside Florida. For these
total truck vehicle-miles traveled within the state. These zones, freight forecasts were developed by factoring existing
freight movements are surveyed as part of Reebie Associates' flows using the growth rates by industry and state provided by
TRANSEARCH database. The FISHFM does not address the Bureau of Economic Analysis's BEA Projections to 2045.
local delivery or service trucks, which primarily serve regional
markets and are best modeled at the regional or urban area
level as part of the MPO planning process. As such, FISHFM Modal Networks
does not attempt to model the two-axle trucks not commonly
FREIGHT MODAL NETWORKS
used in commodity freight shipments.
While the FISHFM is a multimodal commodity model,
Data the assignments were only to be made to a highway network.
Information from the other modal networks, such as dis-
Forecasting Data tances, travel times, or costs, was inferred from the highway
network. The highway network for Florida was the existing
BASE AND FORECAST YEAR SOCIOECONOMIC DATA
Statewide Model highway network to ensure compatibility
The forecasting data include population and employ- with that model. The highway network outside Florida was
ment, used as input to the trip generation step of a freight drawn from the NHPN, as shown in Figure 8.17.
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ever, the 1997 CFS uses a different system, the SCTG. To allow
the direct use of the value information by STCC commodity the
1993 CFS, which also used the STCC system, was used to de-
velop values per ton which were adjusted to 1998 dollars using
the Consumer Price Index for those years.
DAILY VEHICLES FROM LOAD WEIGHTS AND DAYS OF OPERATION
Commodity flow data are given in terms of tons per year.
Because transportation planning functions require model out-
put in the form of vehicles (trucks) per day, it is necessary to
determine the amount of goods carried in a vehicle and the
number of vehicle operation days in a year. Payloads in tons
per day were obtained from the U.S. Census Bureau's VIUS.
Figure 8.17. Highway network for Florida Inter-
modal Statewide Highway Freight Model. Validation Data
Validation data consisted of the truck counts by vehicle
INTERMODAL TERMINAL DATA (SEAPORTS, RAIL YARDS, AIRPORTS) class. Classification truck counts on highways are needed to
separate truck traffic from passenger car traffic. Truck counts
The location of the intermodal terminals (X Y coordinate
by vehicle class were used for the validation of the model-
or zip code) and the activity (ton shipments from/to for both
estimated truck volume. These data are available from the
base year and forecast year) at the major ports and intermodal
1999 AADT Report for Florida and Truck Weight Study Data
terminals by commodity were obtained to locate these facili-
for the U.S. These truck counts include all trucks, not just
ties in FISHFM as special generators. The locations were ob-
freight trucks. The FAF's loaded highway network was used to
tained from the 1999 National Transportation Atlas Data-
estimate the %age of freight trucks observed in truck counts.
bases for the U.S. and Florida, the Strategic Investment Plan
to Implement the Intermodal Access Needs of Florida's Sea-
ports (Part II, U.S. and Florida seaports), Federal Aviation Model Development
Administration Forecasts for the fiscal years 2000-2011, the Software
North America Airport Traffic Report, the Port Facilities In-
ventory (U.S. and Florida water ports), the U.S. Maritime Ad- FISHFM was designed to run using TRANPLAN software
ministration's Office of Intermodal Development, and pub- and FSUTMS scripts.
lished reports from port operators. Two FORTRAN programs were written specifically to run
FISHFM components. The freight trip generation program,
FGEN, generates production and attraction files representing
Model Development Data
the number of tons of goods generated in each zone by com-
The TRANSEARCH commodity flow database as pur- modity group. The mode split program, FMODESP, allocates
chased for Florida was chosen to represent the survey of commodities to modes, and converts annual tons of truck
existing freight flows. The STCC codes in that database were commodities to daily truck trips. All other components of the
used to develop commodity groups for the model, the exist- FISHFM run using the TRANPLAN program within the
ing mode shares were chosen, flows were treated as revealed- FSUTMS structure.
preference surveys, the total tonnage originating in a zone
was chosen to be the production of freight, and the total of Commodity Groups
tonnage destined for a zone was chosen to represent the at-
traction of freight to that zone. The average trip length be- In FISHFM, commodity groups serve a function similar to
tween zones was used for the pattern of trip distribution. that of trip purposes in passenger travel demand models. The
shipments within a commodity group have similar character-
istics. A total of 14 commodity groups were defined for the
Conversion Data FISHFM, as shown in Table 8.36.
VALUES PER TON
Trip Generation
The TRANSEARCH data used for the model is in the STCC
code. The dollar value per ton by commodity can be obtained The FISHFM estimates the total freight tonnage by all
from the Commodity Flow Survey records for Florida. How- modes--truck, carload rail, intermodal rail, water, and air--
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Table 8.36. Commodity groups.
Standard Transportation Commodity
Code Description Codes
1 Agricultural 1, 7, 8, 9
2 Nonmetallic Minerals 10, 13, 14, 19
3 Coal 11
4 Food 20
5 Nondurable Manufacturing 21, 22, 23, 25, 27
6 Lumber 24
7 Chemicals 28
8 Paper 26
9 Petroleum Products 29
10 Other Durable Manufacturing 30, 31, 33-39
11 Clay/Concrete/Glass 32
12 Waste 40
13 Miscellaneous Freight 41-47, 5020, 5030
14 Warehousing 5010
produced (originating) and attracted (terminating) in Florida. independent variable was guided by the employment by SIC in
Production and attraction equations for the 14 commodity the industry associated with the STCC commodity for the pro-
groups were based on population and employment relation- duction equations and with the industries determined by an I-O
ships that were identified by statistical regressions with the model to be the principal consumers of the commodity for the
TRANSEARCH freight database. The trip generation equations attraction equations. Productions and attractions of freight ton-
were produced by a linear regression of observed county pro- nage at ports and airports are treated as special generators.
duction and attraction tonnage by commodity group as the The trip generation equations were programmed into
dependent variable and the employment by industry and/or FGEN for inclusion in the FSUTMS package.
population variable for that county as the independent variable,
as shown in Tables 8.37 and 8.38. The regression assumed a
Trip Distribution
zero-intercept (that is, no freight productions or attractions if
the independent variable is also zero). A variety of independent FISHFM uses a standard gravity model for the distribution
variables were tested to determine the best fit. The choice of of freight tonnage between zones. The average trip lengths for
Table 8.37. Trip production equations.
Code Name Coefficient Variable (Employment)
Commodity Groups
1 Agricultural 45.597 SIC07
2 Nonmetallic Minerals 6,977.771 SUM(SIC10-14)
3 Coal No Production Employment
4 Food 245.464 SIC20
5 Nondurable Manufacturing 90.120 SUM(SIC21,22,23,25,27)
6 Lumber 241.464 SIC24
7 Chemicals 678.583 SIC28
8 Paper 190.814 SIC26
9 Petroleum Products 795.117 SIC29
10 Other Durable Manufacturing 212.202 SUM(SIC30,31,33-39)
11 Clay, Concrete, Glass 1498.501 SIC32
12 Waste 0.500 TOTEMP
13 Miscellaneous Freight 0.599 TOTEMP
14 Warehousing 314.852 SIC50 + SIC51
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Table 8.38. Trip attraction equations.
Code Name Coefficient Variable Coefficient Variable
Commodity Groups
1 Agricultural 23.537 SIC20
2 Nonmetallic Minerals 1461.302 SIC28
3 Coal 178.639 SIC49
4 Food 109.51 SIC51
5 Nondurable Manufacturing 24.698 SIC51
6 Lumber 147.624 SIC25 0.448 Pop
7 Chemicals 83.247 SIC51
8 Paper 23.924 SIC51
9 Petroleum Products 0.228 Pop
10 Other Durable Manufacturing 46.762 SIC 50
11 Clay, Concrete, Glass 2.964 Pop
12 Waste 68.089 SIC33
13 Miscellaneous Freight 2.886 SUM (SIC42,44,45)
14 Warehousing 2.926 Pop
each commodity group were calculated from TRANSEARCH. quency distributions also showed the close correspondence
That average trip length was used as the coefficient of TRAN- between the estimated and actual tables. The average trip dis-
PLAN's gravity model deterrence function. The deterrence tance and deterrence coefficient by commodity group are
function calculates friction factors using an exponential decay shown in Table 8.39.
function of the impedance variable. Distance in miles was The model trip length frequency distributions of all 14
used to determine the impedance variable that produced the commodity groups are reasonable matches to the observed
best fit to the observed trip distributions. A trip length fre- trip length frequencies from the Reebie database. For exam-
quency distribution was prepared for both the estimated and ple, Figure 8.18 presents trip length frequency distributions
the actual trip tables. For all commodity groups except min- for the food commodity group.
erals and coal the R2 was above 0.646. For petroleum and Since the trip distribution used the standard TRANPLAN
nondurable manufactured goods the R2 was above 0.95. The gravity model program, no special programs were needed to
coincidence ratio of the actual and estimated trip length fre- operate with FSUTMS.
Table 8.39. Average trip distance and deterrence coefficient by
commodity group.
Average Deterrence
CG Group Description Distance Coefficient
1 Agricultural 1,260 0.00079
2 Nonmetallic Minerals 332 0.00301
3 Coal 764 0.00131
4 Food 681 0.00147
5 Nondurable Manufacturing 528 0.00189
6 Lumber 606 0.00165
7 Chemicals 790 0.00127
8 Paper 406 0.00246
9 Petroleum Products 768 0.00130
10 Other Durable Manufacturing 712 0.00140
11 Clay/Concrete/Glass 244 0.00410
12 Waste 1,034 0.00097
13 Miscellaneous Freight 748 0.00134
14 Warehousing 250 0.00400
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Trips (in Percent)
12
Model
Reebie
10
8
6
4
2
0
100 500 900 1,300 1,700 2,100 2,500 2,900 3,300 3,700
300 700 1,100 1,500 1,900 2,300 2,700 3,100 3,500 3,900
Minutes
Figure 8.18. Reebie versus model TLF distribution.
Mode Split/Daily Truck Conversion The explanatory variables applied in the model were the nat-
ural log of travel time multiplied by commodity value per ton
FISHFM was developed to estimate annual tons shipped by
and travel cost. For the travel time variable, the highway
truck, bulk/carload rail, container/intermodal rail, air, and
uncongested (free-flow speed) skim file, as created by
water. The mode split model is in the form of an incremental
TRANPLAN, was used. The highway cost is $0.0575 per mile
logit mode choice model. This model pivots from the base
traveled. The carload rail cost is $12 + $0.025 per mile. The in-
mode shares as identified in the TRANSEARCH database.
termodal rail cost is $26 + $0.028 per mile. The highway time is
The base water and air mode splits are assumed to remain
INT((distance/50 + 8)/18) * 8 + distance/50, which represents
unchanged. For all O-D pairs, the mode share for each other
travel at 50 mph and an eight-hour rest period after every
mode (truck, carload rail, and intermodal rail) for each com-
10 hours of travel, in accordance with the hours of service reg-
modity is the base year mode share as adjusted by an incre-
ulations. The carload rail time is 60 hours plus distance/20 mph.
mental logit model. The coefficients of the utility equation
The intermodal rail time is 24 hours + distance/22.75 mph.
were calculated using ALOGIT and the TRANSEARCH data
The coefficients of the utility equation are given in Table
as a revealed-preference survey.
8.40. For commodity groups 2, 3, and 13 (minerals, coal, and
The mode split model is an incremental logit model, as
waste, respectively) no truck tonnage is given in the base year,
shown below.
the truck mode split is 0% for all alternatives, and no coeffi-
Si Exp ( U i ) cients are given. For commodity groups 12 and 14 (clay/
Si = J concrete and warehousing, respectively), all tonnage is by
Exp(U j ) truck in the base year, the truck mode split is 100% for all
I =1
alternatives, and no coefficients are given. While the utility
where constants for carload rail and intermodal rail differ, the util-
Si = New share of mode i; ity coefficients for time and cost are the same for both carload
Si = Original share of mode i; and intermodal rail.
Ui = Utility of mode i in the choice set J (j = 1,2,3, . . .,J); FISHFM develops daily truck assignments. It is therefore
= Modal Constanti + bv × (Explanatory Variableiv; and necessary to convert the annual truck table of tonnages to
b = Coefficient for Explanatory Variable (e.g., travel time).
v
daily truck trips. The table of annual shipments of tonnage by
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Table 8.40. Mode choice model utility coefficients.
Commodity Intermodal Carload
Group Value per Ton Constant Constant Time Cost
1 $171.49 -2.05 -0.69 -0.00757 -0.00417
2 $24.33 No Truck
3 $27.01 No Truck
4 $684.14 -1.85 -0.15 -0.00194 -0.00189
5 $7,175.17 2.86 3.92 -0.00069 0.0281
6 $276.15 -0.68 -2.47 -0.00473 -0.00388
7 $865.91 -3.37 -0.96 -0.00092 -0.00861
8 $1,041.00 -0.45 -1.75 -0.00126 -0.00240
9 $175.93 3.00 9.16 0.000217 0.0868
10 $5,143.68 -0.48 1.88 -0.00048 0.0145
11 $103.62 -1.57 1.72 -0.02075 0.0164
12 $4,612.67 All Truck
13 $7,264.31 No Truck
14 $1,618.00 All Truck
truck between the origins and destinations is converted into increased as distance increased. The growth function is
truck trips using payload factors established from the Florida defined as follows:
data in VIUS. These factors are specific to each commodity
Payload Factor = exp (bo + (b1 * Distance))
group and vary by the distance traveled between zones. The
factors include the percentage of mileage that a truck travels This modification ensured a better fit with observed truck
empty, based on VIUS. flows. The calibrated tons per daily truck by commodity
During the model validation process, truck conversion group are shown in Table 8.41.
factors were modified by smoothing the values. The In order to implement the mode split component and the
smoothing method was used to fit values to a growth func- conversion to daily truck trips in FSUTMS, a special program
tion as a calibration parameter so that the average truck load known as FMODESP was written in FORTRAN.
Table 8.41. Calibrated tons per daily truck by commodity group.
Miles
Greater
Commodity Group Less Than 50 50 to 100 100 to 200 200 to 500 Than 500
Agricultural 13.59 16.04 18.92 22.32 26.34
Nonmetallic Minerals 19.35 20.92 22.63 24.46 26.45
Coal 19.35 20.92 22.63 24.46 26.45
Food 12.19 14.92 18.28 22.38 27.40
Non-durable Manufacturing 3.94 5.79 8.51 12.51 18.38
Lumber 10.80 14.12 18.46 24.14 31.57
Chemicals 10.93 13.29 16.15 19.63 23.87
Paper 15.53 17.99 20.85 24.16 27.99
Petroleum Products 24.58 24.99 25.40 25.82 26.24
Other Durable Manufacturing 6.32 8.92 12.58 17.76 25.07
Clay/Concrete/Glass 19.57 21.29 23.16 25.20 27.41
Waste 12.45 14.99 18.06 21.76 26.21
Miscellaneous Freight 7.79 10.49 14.13 19.02 25.62
Warehousing 8.25 9.93 11.95 14.38 17.30
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Table 8.42. Ratio of estimated volume-to-count by facility type.
Number of
Links with Estimated Volume/
Area Type Facility Type Counts Volume Truck Count Count Ratio
10 10 228 714,290 712,350 1.00
10 20 2 395 410 0.96
10 60 12 24,373 16,662 1.46
Total 242 739,058 729,422 1.01
Assignment The volume-over-count ratios by facility type are pre-
sented in Table 8.42. The overall volume-to-count ratio is a
The daily truck trip table is assigned to the highway net-
perfect match for interstate freeways (FT 10) with a ratio of
work, which includes the Florida Intrastate Highway System
1.00. The highest is for toll roads (FT 60), at 1.46. The lowest
plus major arterials and collectors and the skeletal network
is for other freeway types (FT 20), at 0.96 where the values of
developed from the National Highway Planning Network
volumes and counts are low. The overall ratio of 1.01 indi-
outside Florida. The North American network was connected
cates that the model performs extremely well relative to these
to the Florida Statewide Model network at nodes shared by
performance measures. Table 8.43 shows the volume over
external station connectors in the Statewide Model network,
count ratios for major interstate freeways (I-75, I-95, and
as shown in Figure 8.17. The freight trucks are assigned based
I-10) at the Florida state line. Other major statewide screen-
on free flow paths and preloaded to the network prior to any
line volume-over-count ratios are presented in Table 8.44.
assigning of general vehicle trips.
The majority of estimates were within 10% of the observed
screenline volumes. The RMSE summary is shown in Table
Model Validation 8.45. The overall RMSE is well below the maximum desirable
percent RMSE established for urban passenger models by
Model validation was completed with the same data used
FDOT.
in developing the models. During the model validation
process, the need to calibrate the model was studied and iden-
tified for each model step, including trip generation, trip
Model Application
distribution, mode split/truck conversion, and truck assign-
ment. Validation of the assignment of daily freight trucks was The FISHFM is still under development and is being con-
compared against observed truck counts. verted to a new statewide model zone structure and network. It
is being considered for use in a variety of applications including:
Trip Assignment
· Existing and forecast productions and attractions of an-
The truck volumes loaded in the model were validated nual freight tonnage for each TAZ in Florida for 14 specific
against the truck counts on major corridors, across the screen commodities;
lines and external stations. Estimates such as VMT, vehicle- · The existing and forecast O-D table of annual freight ton-
hours traveled by truck, and RMSE statistics were reviewed nage moving between TAZs and the external zones cover-
and compared with existing statewide freight models and ing North America, for 14 specific commodities;
urban freight/truck models. The model was validated on cor- · The existing and forecast table of annual freight tonnage by
ridors, screen lines, area types and facility types as well. mode and by commodity derived from the total O-D table;
Table 8.43. Florida state line volume/count ratio.
Interstate Freeway Model Volume Observed Count Volume/Count
I-75 10,175 9,600 1.06
I-95 4,125 4,350 0.95
I-10 4,062 4,450 0.91
Total 18,362 18,400 1.00
