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cars. SCAG subsequently conducted an arterial average speed to the FHWA Freight Analysis Profile for Indiana, in 1998
study in fiscal year 2003-2004. For the HDT model as pre- over 698 million tons of goods worth more than $398 billion
sented in this case study, validation, the ratio of speed of were moved to, from, within, and through Indiana, traveling
HDTs compared to passenger cars on arterials was assumed by highway, rail, water, and air.21 This represents almost 5%
to be similar to the same relationship observed on freeways. of the freight tonnage and over 4% of the freight value moved
in the United States. In the early 1990s, in order to better
understand freight movements, the Indiana Department of
Model Application
Transportation (InDOT) sponsored a research project con-
The SCAG HDT Model was initially used to forecast truck ducted by the Transportation Research Center of Indiana
volumes by truck class for the year 2020, as shown in Table 8.28. University. The goal of the project was to create a database
that would include the flows of manufactured goods, major
grains, and coal along the state's transport networks and the
Performance Measures and Evaluation
use of that data to develop a series of models to estimate
The SCAG HDT Model presents no special performance the future flows of freight. If the research was successful, the
measures. The model is used to produce volume, speed, and results were to be included in InDOT's comprehensive Indi-
air emission forecasts. While truck performance results for ana Statewide Travel Demand Model.
these measures specifically are not produced, since heavy The Indiana Commodity Transport Model was created in
duty trucks are maintained as a separate trip type, it would be 1993 using the 1977 Bureau of Transportation Statistics CFS
possible to use the model to produce those standard perfor- and was updated in 1997 using the 1993 CFS.22 The 1993 CFS
mance measures outputs for only those truck trips. showed that in that year about $179 billion of goods weigh-
ing 286 million tons originated in Indiana. These goods
8.8 Case Study Indiana accounted for about 3% of the value and weight of total U.S.
Commodity Transport Model shipments. Major commodities originating in Indiana by
value included transportation equipment, metal products,
Background food, electrical machinery, and chemicals. Major commodi-
ties by weight included petroleum or coal products, minerals,
Context
farm products, and metal products. About three-quarters of
Indiana's transportation network, shown in Figure 8.14, these commodities (by value and weight) moved by truck,
moves a tremendous volume of goods each year. According with lesser amounts moving by rail (7% by value and 15% by
Table 8.28. Comparison of 2020 and 1995 forecast truck volumes on regional model
screenlines.
2020 Model Volume 1995 Model Volume Difference Allowable per
Screenline (ADT) (ADT) (2020-1995) NCHRP
1 120,690 73,778 46,912 63%
2 196,468 118,760 77,708 65%
3 111,695 59,610 52,085 87%
4 79,241 61,901 17,340 28%
5 144,770 93,010 51,760 56%
6 80,250 73,778 6,472 9%
7 83,769 46,866 36,903 79%
8 141,051 82,117 58,934 71%
9 88,972 28,712 60,260 210%
10 30,501 23,118 7,383 32%
11 20,676 14,879 5,797 39%
Total 1,098,083 676,529 421,554 62%
Source: Southern California Association of Governments Heavy-Duty Truck Model.
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Source: ESRI data and maps 2002, prepared by Cambridge Systematics, Inc.
Figure 8.14. State of Indiana.
weight) and by parcel post, U.S. Postal service, and courier General Approach
services (7% by value). The CFS also shows that in 1993 about
28% of Indiana's shipments by value and 56% of its ship- Model Class
ments by weight were bound for destinations within the state. The Indiana Commodity Transport Model is a four-step
For shipments to other states, the main destinations by value commodity flow class of model based on the traditional four-
were Michigan, Illinois, Ohio, California, and Kentucky. step transportation planning model commonly used for
By weight, the major destinations were Michigan, Ohio, passenger and total truck forecasting applications. A detailed
Kentucky, and Louisiana. description of the four-step commodity class of model is pro-
vided in Section 6.4.
Objective and Purpose of the Model
Modes
InDOT's primary objective in supporting the research
project was the creation of a model or forecasting tool capa- Following the modal definition in the CFS, the Indiana
ble of estimating future flows of commodities on Indiana's Commodity Transport Model considers nine single mode
rail and highway networks, from which a general transporta- categories, as shown in Table 8.29. The model does not count
tion model for the state could be developed. traffic passing through Indiana or traffic originating outside
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Table 8.29. Modal categories.
Single Modes Multiple Modes
Parcel/Courier Private Truck and For-Hire
U.S. Postal Service Truck and Air
Private Truck Truck and Rail
For-Hire Truck Truck and Water
Air Truck and Pipeline
Rail Rail and Water
Inland Water Inland Water and Great Lakes
Great Lakes Inland Water and Deep Sea
Deep Sea Water
the United States. Truck as the primary or part of a multiple to produce vehicle flows. The model components are written
mode of freight shipments was used by about 77% of ship- in several different software programs and are manually
ments originating in Indiana in terms of value. Rail linked together.
accounted, solely or with other modes, for about 7% of the
traffic based on value and 15% based on weight. Air freight
Flow Units
(excluding parcels) and truck-air accounted for 2% of ship-
ments based on value and less than 0.1% based on weight. The Indiana model focuses primarily on daily interstate
Following the CFS, the Indiana model considers eight mul- and intercounty commercial transport flows, mainly large
tiple mode categories. However, the 1993 CFS data indicated trucks and rail cars moving on the regional transportation
that intermodal traffic in Indiana was insignificant, repre- system between regions in Indiana and the rest of North
senting only about one-quarter of 1% based on tonnage but America. The model does not address goods movement asso-
over 3.2% based on value. ciated with the service transport sector (such as, commercial
laundry vehicles, plumbers, lawn care vehicles), nor does it
consider movements by household moving vans.
Markets
The main component of commercial vehicle traffic in-
Data
cluded in this model was interregional freight shipments to,
from, and within Indiana, although the model was not limited Forecasting Data
to Indiana traffic only, since a significant portion of the com-
BASE AND FORECAST YEAR SOCIOECONOMIC DATA
modity traffic in Indiana does not have an origin or destina-
tion in the state. The study includes not only the 92 counties The Indiana model was calibrated and validated to 1993
of Indiana but several major terminals outside the state in- base year data. Forecasts were made for future years, 1998,
cluding all of the remaining contiguous 47 states as well as 2005, and 2015. Future year input data was primarily com-
additional nodes for the states bordering Indiana, for a total of posed of population and employment forecasts from Woods
145 nodes or centers of freight activity. & Poole.
Framework EXTERNAL MARKETS
The Indiana Commodity Transport Model was developed Much of the commodity traffic in Indiana has neither an
as a research project to prove the concepts presently being in- origin nor a destination in the state, but instead represents
troduced into Indiana's Statewide model. The model struc- goods or materials passing through the state. This through
ture follows the basic four-step transportation planning traffic may contribute little to the state's economy, but it adds
model structure typical of passenger models. Trip generation to urban congestion, air pollution, rail traffic, and wear and
and trip distribution components utilize tons of commodi- tear on highways. To address the impact of through traffic,
ties rather than persons and the mode split step distributes the commodity flow model includes, in addition to the 92
tons to the various modes or mode combinations available counties of Indiana, nine other nodes or terminals represent-
for shipments. These tonnage trip tables are then converted ing portions of the adjacent states of Ohio, Illinois, Kentucky,
to trucks or rail cars and assigned to the appropriate networks and Michigan and the single zones for the remaining 43 con-
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tinental states and the District of Columbia. Base year mod- roads, the main sources used for the Indiana study. These
els for all 145 zones relied on data from the 1993 CFS and data are considered proprietary in many cases and therefore
were supplemented with information from the 1977 Census were not reported or included in the model.
of Transportation. Forecasts of future year socioeconomic
data used to generate external trip-making levels were based
Model Development Data
on 1992 projections by Woods & Poole.
The 1993 CFS that forms the basis for this model is shipper-
based and therefore only includes data for U.S. shippers.
Modal Networks Data on imports to Indiana are not included, although some
FREIGHT MODAL NETWORKS estimates were made to account for this gap in the data. Other
components, such as vehicle movements associated with the
The highway network for the Indiana Commodity Transport service transport sector and movements by household mov-
Model includes all major facilities within a 200-mile radius of ing vans, also were not included.
Indianapolis and, for roads outside Indiana, the FHWA's 1992 The main data source for the development of the original
digital highway network, which covers only major interstate trip production and attraction models was the 1977 Census
highways connecting the lower 48 states. To provide even of Transportation and the Commodity Flow Survey. This
greater detail in order to match the county-level zone system source was chosen because no other comprehensive data were
within Indiana, roadway detail at the State Roadway Inventory available at the time the model development began. The 1993
level was included. The resulting network consists of 34,154 Census of Transportation and CFS was underway at the
links and 31,557 nodes, as shown in Figure 8.15. beginning of the project but results were not available until
late in the development phase; ultimately, the 1993 data
were used to update and validate the traffic distribution mod-
INTERMODAL TERMINAL DATA
els that describe the flows into, through, within, and out of
Very little data on intermodal freight transported through Indiana. In addition, the model development made use of
terminals was available from the 1993 CFS and the Bureau of various years of County Business Patterns, U.S. Census
Transportation Statistics' Carload Waybill Sample for rail- Bureau data, and Carload Waybill Sample data.
Source: W.R. Black, Transport Flows in the State of Indiana: Commodity Database Development and Traffic Assignment,
Phase 2, Bloomington, Indiana: Transportation Research Center, Indiana University, 1997.
Figure 8.15. Highway network for the Indiana Commodity Transport Model.
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Little data was available from CFS regarding the destina- Validation Data
tions of individual commodity groups for Indiana shipments.
The 1993 CFS data were used to validate estimated com-
However, destination data for all shipments were available
modity flow tables to, from, and within the 145 zones within
and indicated that the major destination in terms of both
the model. Assigned truck volumes from the model were
value and weight was Indiana itself, which is common among
compared against InDOT traffic counts from 1991 to 1994.
most states. Destinations in terms of value for shipments out
No route segment-specific data on rail flows were available
of state were Michigan, Illinois, Ohio, California, and
for comparison of assigned rail volumes. A visual examina-
Kentucky. In terms of tonnage, the major destinations out of
tion of rail flows was made to assess their reasonableness.
state were Illinois, Michigan, Ohio, Kentucky, and Louisiana.
Model Development
Conversion Data
Software
Commodity density factors by commodity were developed
for rail from the Waybill Sample, adjusted for destination (in- Most of the model components, including network cre-
bound or outbound). This process yielded tons by commod- ation and traffic assignment, were developed and operate
ity per carload. These factors were used to develop density within the GIS-based TransCAD planning software. Other
factors for trucks by multiplying by 0.40, the relative differ- independent estimation procedures also were utilized, such
ence in loads between rail cars and trucks. as multivariate analysis and entropy-based gravity model
To convert annual tons to daily trucks, factors based on algorithms using specially developed FORTRAN programs.
data within the Highway Capacity Manual Special Report 209
were used. A daily to annual factor of 306 days was derived for
Commodity Groups/Truck Types
weekday traffic. Multiplying the estimated weekday traffic by
0.44 yielded an approximation for weekend truck traffic. For this study, all two-digit categories of the STCC were
Table 8.30 shows the payload factors used for converting ton- examined in terms of their importance to Indiana's econ-
nage to truck volumes. omy. A set of 18 commodity groups was identified. One
Table 8.30. Traffic density factors for rail cars and motor carriers
by commodity.
Commodity Import Export Weighted Rail Weighted Truck
STCC Rail Traffic Rail Traffic Density (Tons) Density (Tons)
01 94.90 96.20 96.13 38.44
11 100.60 99.10 100.42 40.17
14 97.10 97.40 97.20 38.88
20 77.35 80.36 79.52 31.81
22 25.00 15.00 18.33 7.33
23 N/A N/A *10.00 *4.00
24 73.88 55.50 72.27 28.91
25 N/A 15.00 15.00 6.00
26 64.82 50.64 62.10 24.84
28 85.11 90.11 87.58 35.03
29 63.20 77.16 65.90 26.36
32 86.70 77.10 81.15 32.46
33 87.48 85.21 85.82 34.33
34 28.40 16.16 19.76 7.90
35 68.75 21.70 28.42 11.37
36 18.80 16.25 16.69 6.68
37 19.93 23.40 22.50 9.00
40 75.40 82.60 78.47 31.39
**50 92.85 14.88 86.56 34.62
* Estimated Values
** STCC 50 represents STCC 21, 27, 30, 31, 38 and 39.
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additional group of five commodities was aggregated to represent the consumer market to account for locally con-
a single category called STCC 50. As used in the Indiana sumed goods.
Commodity Transport Model, STCC 50 includes all durable Traffic attraction models are based on the assumption that
and nondurable manufactured commodities not separately the flows of manufactured goods to a particular market are a
processed. It differs from the definition of STCC 50 as sec- function of the demand for that product in two markets: per-
ondary traffic to warehousing and distribution centers as sonal consumers and industrial consumers. In the former
used in TRANSEARCH and the Freight Analysis Frame- market, population is the key variable. In the case of indus-
work. In addition, movements by the U.S. Postal Service trial consumers, employment is again key.
and overnight express mail operations also were included in At the time the Indiana Commodity Transport Model was
the analysis. being developed, data from the 1993 CFS was unavailable.
Based on the CFS, commodity flows originating in Indiana Most of the model's components were therefore developed
in 1993 were valued at $178.7 billion and exceeded 280 mil- using the 1977 dataset. Population estimates derived from
lion tons. By weight, they consisted primarily of petroleum U.S. Census Bureau figures from 1977 and employment data
and coal products (21.9%), nonmetallic minerals (20.1%), derived from 1977 County Business Patterns were used to
farm products (14.0%), primary metal products (9.8%), develop models based on these 1977 production and attrac-
stone, clay and glass products (7.7%), food and kindred tion levels of manufactured goods. Models of nonmanufac-
products (7.4%), and chemicals and allied products (4.2%). tured goods (coal, nonmetallic minerals, farm products, and
The major commodity groups in Indiana in 1993 are shown waste) were developed using the 1993 CFS and Census Bureau
in Table 8.31. data. Table 8.32 shows these models along with an indicator
of their accuracy. Table 8.33 describes the model variables.
Trip Generation
Trip Distribution
Traffic production models are based on the assumption
that employment in a particular sector is an accurate indica- The Indiana Commodity Transport Model uses a standard
tor of that sector's production. In these models, the key vari- gravity model or entropy model to distribute annual freight
able is employment. In some cases, population also is used to tonnage between origins and destinations in the United States
Table 8.31. Major commodity groups in Indiana (1993).
Value
Description STCC Code (Millions of Dollars) Tons (Thousands)
Farm Products 01 $5,794 39,902
Coal 11 281 10,759
Nonmetallic Minerals 14 463 57,341
Food and Kindred Products 20 16,958 21,039
Basic Textiles 22 275 93
Apparel 23 7,795 553
Lumber and Wood Products 24 3,235 4,131
Furniture and Fixtures 25 3,120 734
Pulp and Paper Products 26 3,194 2,814
Chemicals and Allied Products 28 11,474 11,957
Petroleum and Coal Products 29 9,008 62,500
Stone, Clay and Glass Products 32 2,748 21,972
Primary Metal Products 33 17,485 27,881
Fabricated Metal Products 34 10,363 4,572
Machinery (except Electrical) 35 9,504 1,023
Electrical Machinery 36 15,914 1,909
Transportation Equipment 37 34,401 6,731
Waste and Scrap Material 40 703 4,474
Other Manufactured Productsa 50 14,811 2,421
Source: Bureau of Transportation Statistics, 1993 Commodity Flow Survey.
a Category 50 includes STCC 21, STCC 27, STCC 30, STCC 31, STCC 38, and STCC 39.
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Table 8.32. Traffic generation models.
Model Number Model Equation Adjusted R Squared
(1) Prod01 = 1445 .523 Agser +.0048 Cash 0.562
(2) Attr01 = .819 Prod01 0.660
(3) Prod11 = 7.6 Coal 0.650
(4) Attr11 = 3.1 Coal + 5.3 Min 0.657
(5) Prod14 = .078 Man 0.658
(6) Attr14 = .997 Prod14 0.977
(7) Prod20 = .282 Food 0.965
(8) Attr20 = .832 Pop + .162 Food 0.965
(9) Prod22 = .016 Tex 0.931
(10) Attr22 = .003 App + .0001 All 0.743
(11) Prod23 = .004 App 0.919
(12) Attr23 = .002 App + .011 Pop 0.926
(13) Prod24 = .668 Lum 0.808
(14) Attr24 = .728 Prod24 0.805
(15) Prod25 = .017 Furn 0.906
(16) Attr25 = .033 Pop + .002 Furn 0.960
(17) Prod26 = .103 Pulp + .056 Lum 0.886
(18) Attr26 = .085 Pulp + .002 Furn 0.953
(19) Prod28 = .150 Chem + 1.164 Pet 0.758
(20) Attr28 = .077 Chem + .455 Pet + .683 Pop 0.851
(21) Prod 29 = 6.857 Pet 0.945
(22) Attr29 = 4.007 Pet + 1.881 Pop 0.938
(23) Prod32 = 2.882 Pop 0.851
(24) Attr32 = 2.914 Pop 0.871
(25) Prod33 = .085 Met 0.982
(26) Attr33 = .093 Met + .061 Fab 0.923
(27) Prod34 = .013 Met + .034 Fab 0.927
(28) Attr34 = .035 Fab 0.861
(29) Prod35 = .013 Mac 0.883
(30) Attr35 = .010 Mac 0.878
(31) Prod36 = .004 Met + .004 Fab + .003 Elec 0.826
(32) Attr36 = .005 Fab + .034 Pop 0.915
(33) Prod37 = .040 Tran 0.753
(34) Attr37 = .027 Tran 0.837
(35) Prod40 = .00048 Pop 0.704
(36) Attr40 = .0067 Man 0.791
(37) Prod50 = 1.097 Attr50 0.858
(38) Attr50 = .245 Pop 0.857
Source: W.R. Black, Transport Flows in the State of Indiana: Commodity Database Development and
Traffic Assignment, Phase 2, Bloomington, Indiana: Transportation Research Center, Indiana
University, 1997.
for the year 1993. The cost or impedance factor for the grav- Dk = the amount of a given commodity demanded by des-
ity formulation was based on the straight-line distance be- tination k; and
tween zones. The model has the general form: cjk = a measure of the cost or impedance of moving from j
to k.
Sjk = Aj Bk Oj Dk exp (- cjk)
In addition,
where
Sjk = the amount of a given commodity shipped from ori- Aj = [ Bk Dk exp ( cjk)]-1
gin j to destination k;
and
Oj = the amount of a given commodity available for ship-
ment at origin j; Bk = [Aj Oj exp ( cjk)]-1
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Table 8.33. List of employment variables used in trip generation equations.
Variable Name Description SIC Code
Agser Employment in Agricultural Services 07
All Total Employment N/A
App Employment in Apparel and Other Textile Products 23
Cash Gross Cash Receipts (in $1,000s) from Farming N/A
Chem Employment in Chemicals and Allied Products 28
Coal Employment in Coal Mining 11
Elec Employment in Electrical and Electrical Equipment 36
Fab Employment in Fabricated Metal Products 34
Food Employment in Food and Kindred Products 20
Furn Employment in Furniture and Fixtures 25
Lum Employment in Lumber and Wood Products 24
Mac Employment in Industrial Machinery and Equipment 35
Man Employment in Manufacturing 02 and 03
Met Employment in Primary Metal Industries 33
Min Employment in Nonmetallic Minerals, except Fuels 14
Pet Employment in Petroleum and Coal Products 29
Pop Total Population N/A
Pulp Employment in Paper and Allied Products 26
Tex Employment in Textile Mill Products 22
Tran Employment in Transportation Equipment 37
Source: W.R. Black, Transport Flows in the State of Indiana: Commodity Database Development and
Traffic Assignment, Phase 2, Bloomington, Indiana: Transportation Research Center, Indiana
University, 1997.
The development of the model also used actual data for available for movement. As shown in Table 8.29, the modal
Indiana to refine or calibrate the estimates of county-to- split model (NEWMODE) considered nine individual modes
county flows. These refinements were meant to ensure that: and eight multiple mode categories. Each of the 17 modes was
further divided into nine distance-based categories: less than
1. Total flows from all states within the gravity model were 50 miles, 50 to 99 miles, 100 to 249 miles, 250 to 499 miles,
equal to actual traffic productions by manufacturing 500 to 749 miles, 750 to 999 miles, 1,000 to 1,499 miles, 1,500
category for those states. to 1,999 miles, and 2,000 miles or more. Base year weights or
2. Total flows to and from Indiana, by commodity, as gener- probabilities were developed using the 1993 CFS for each of
ated by the model, were equal to actual flows reported in the market-segmented modes and applied to future year trip
the commodity census. tables to create future year trips by mode. The model allo-
3. Total flows generated by each state were equal to national cated future flows based on current mode splits in each of
totals. those distance classes.
Table 8.34 shows the average shipping distance per ton of
Flow Unit and Time Period Conversion
commodity for estimated and actual conditions for Indiana
and the rest of the United States. Commodity density factors by commodity were developed
for rail from the Carload Waybill Sample, adjusted for desti-
nation (inbound or outbound). This process yielded tons by
Commodity Trip Table
commodity per rail carload. As shown in Table 8.35, these
Not applicable for the Indiana model. Commodity tables, factors were used to develop density factors for trucks by
the CFS, and Carload Waybill Samples were not used directly multiplying by 0.40, the relative difference in loads between
but supported the development of model parameters. rail cars and trucks.
To convert annual tons to daily trucks, factors based on
data within the Highway Capacity Manual Special Report 209
Mode Split
were used. A daily to annual factor of 306 days was derived for
A computer model was written to distribute traffic flows weekday traffic. Multiplying the estimated weekday traffic by
generated by the gravity model among the various modes 0.44 yielded an approximation for weekend truck traffic.
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Table 8.34. Traffic distribution model results (average shipper distance per ton
of commodity).
Commodity STCC U.S. Average Indiana Average
Actual Modeled Actual Modeled
(1) 434 434 435 432
(11) 432 432 85 436
(14) 87 116 44 122
(20) 315 311 333 311
(22) 458 445 236 489
(23) 658 420 391 397
(24) 182 190 220 222
(25) 591 592 794 563
(26) 464 313 313 314
(28) 434 345 280 294
(29) 152 153 89 140
(32) 105 202 124 189
(33) 365 365 356 361
(34) 359 358 342 345
(35) 559 500 472 473
(36) 649 505 481 483
(37) 560 487 449 446
(40) 211 211 181 243
(50) 560 507 426 465
Source: W.R. Black, Transport Flows in the State of Indiana: Commodity Database Development and Traffic Assignment,
Phase 2, Bloomington, Indiana: Transportation Research Center, Indiana University, 1997.
Table 8.35. Traffic density factors for rail cars and motor carriers by commodity.
Commodity Rail Traffic Weighted Rail Weighted Truck
STCC Import Export Density (Tons) Density (Tons)
01 94.90 96.20 96.13 38.44
11 100.60 99.10 100.42 40.17
14 97.10 97.40 97.20 38.88
20 77.35 80.36 79.52 31.81
22 25.00 15.00 18.33 7.33
23 N/A N/A 10.00a 4.00a
24 73.88 55.50 72.27 28.91
25 N/A 15.00 15.00 6.00
26 64.82 50.64 62.10 24.84
28 85.11 90.11 87.58 35.03
29 63.20 77.16 65.90 26.36
32 86.70 77.10 81.15 32.46
33 87.48 85.21 85.82 34.33
34 28.40 16.16 19.76 7.90
35 68.75 21.70 28.42 11.37
36 18.80 16.25 16.69 6.68
37 19.93 23.40 22.50 9.00
40 75.40 82.60 78.47 31.39
50b 92.85 14.88 86.56 34.62
