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APPENDIX D
Annotated Bibliography of Statewide Freight Forecasting
This appendix presents material originally developed for OVERVIEWS OF STATEWIDE TRAVEL
NCHRP Project 8-43, "Methods for Forecasting Statewide FORECASTING
Freight Movements and Related Performance Measures."
The appendix was written by Alan J. Horowitz, K. Ian There have been two notable attempts to define the scope and
Weisser, Cheng Gong, and Joe Blakeman. content of a statewide freight model. The first attempt was
NCHRP Report 260: Application of Statewide Freight De-
mand Forecasting Techniques, and the second attempt was
the Guidebook on Statewide Travel Forecasting. Before dis-
INTRODUCTION
cussing these two reports, it is necessary to define "OD table
A review of current planning practice indicates that the factoring and assignment" as a widely used methodology of
field of statewide travel forecasting is still in flux; a con- statewide freight forecasting.
sensus does not exist as to the best way to construct a
model for any given set of policy needs or planning OriginDestination Table Factoring
requirements. States modeling efforts fall into one of these and Assignment
four categories:
A frequently used method of freight forecasting can be de-
scribed as origindestination (OD) table factoring and as-
1. No model--Specialized studies work from existing
signment. This method (with some variation) has been used
proprietary or public databases or from locally col-
by many states, the I-10 corridor study, and FHWA's Freight
lected data.
Analysis Framework (FAF). The most prevalent application
2. Truck model--Truck models are used to account for
of this method follows these general steps.
the congestion effects of freight on highways or to help
determine equivalent single-axle loads (ESALs) for
1. Obtain base year OD tables (in tons per year) by com-
pavement design purposes.
modity and by mode that matches the desired traffic
3. Commodity-based four-step model--Commodity-
zone system. Typically, flows between external zones
based models follow the same steps as passenger mod-
that do not pass though the internal portions of the net-
els, except that trip generation is performed for weight
work are excluded.
of commodities by groups of commodities.
2. Obtain base year and future year levels of economic
4. Economic activity model--Economic activity mod-
activity (by industrial sector) for all zones.
els trace the flows of commodities between eco-
3. Establish a mapping between industrial sectors and
nomic sectors and between zones. Economic activity
commodity categories, such that a percent increase in
models are often implemented within a framework
an industrial sector can be associated with a percent in-
that also forecasts the locations of employers and
crease in a commodity.
residences.
4. Determine the percent increase in each commodity's
origins and destinations by applying growth factors
There is considerable variation in how statewide freight obtained in steps 2 and 3.
models have been implemented. 5. Apply Fratar factoring to each OD table to achieve the
percent increases determined in step 4.
In addition to those models currently being used by 6. Determine the number of vehicles necessary to carry
states there is a large variety of models that have been each OD flow for one equivalent weekday.
implemented for such purposes as international trade, 7. Assign each factored vehicle trip table to its respective
national trade, energy policy, and corridor studies. Fur- modal network.
thermore, there are older statewide freight models that
have been inactivated, models currently under develop- This method assumes that the mode split for any given
ment, international freight models, general guidelines as to commodity and for any given OD pair is a constant. Any
how statewide freight models may be built, numerous aca- modal shifts that occur in this method are the result of growth
demic studies that attempt to improve freight forecasting (or decline) or spatial shifts in economic activity and the con-
methodology, timeseries methods of directly forecasting sequential effects on commodity production and consump-
vehicular traffic on facilities, and research and develop- tion patterns. Shifts owing to changes in costs, supply chain
ment intended for passenger forecasting that carries over practices, shipping and transfer times, or vehicle technology
to freight. are not included.
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The method further assumes that the production, con- data are now obsolete, but the terms included in the cost
sumption, and shipping characteristics of commodities re- equations are still relevant.
main unchanged. Such assumptions can be eliminated by
careful consideration of changes in (a) shipping density of Truck cost is composed of insurance, driver wages and
commodities, particularly the result of packaging materials; benefits, driver expenses, fuel, overhead, licenses and per-
(b) worker productivity when economic activity forecasts are mits, ton-mile taxes, federal highway user taxes, tractor cap-
given in number of workers in an industry; (c) value per ton ital cost, tractor maintenance, tractor tire cost, trailer capital
when economic activity forecasts are given in monetary cost, trailer maintenance, trailer tire cost, stop and delay
units; (d) the routing patterns of the supply chain; and (e) costs, and terminal cost.
competitiveness of modes or intermodal combinations to
carry specific commodities. The recommended method of rail costing was the Uniform
Rail Costing System developed for the Interstate Commerce
Those who have tried this method have had to account for Commission, which needed a fair method for setting tariffs.
important commodity flows that were not included in the Few details are provided on the operation of the computer
original OD tables. In addition, it is necessary to adjust for program, which performs its cost estimates by referencing an
the number of empty vehicles. extensive database of actual rail costs. The program reports
line-haul costs, terminal costs, freight car costs, cost of spe-
cialized services, and costs of loss and damage.
NCHRP Report 260: Application of Statewide
Freight Demand Forecasting Techniques Barge cost is composed of many components including
terminal costs, ownership costs, towing costs, and switching
Memmott, F., NCHRP Report 260: Application of Statewide costs. The barge cost module assumes an empty backhaul.
Freight Demand Forecasting Techniques, Transportation Highly detailed information is required about the conditions
Research Board, National Research Council, Washington, of the shipment, including the specific origin and destination,
D.C., Sep. 1983, 210 pp. tons per barge, towboat horsepower, barge investment, inter-
est rates, and user fees.
This report was the first major effort to devise a standard
method for statewide freight forecasting. The proposed Statistical rate equations to estimate tariffs for both truck
method was based on the generalized procedure of OD table (private, truck-load, and less-than-truck-load) and rail (trailer-
factoring and assignment. A considerable amount of space in on-flatcar and carload) are provided. These equations use
the report was devoted to effectively exploiting existing data such independent variables as distance, shipment size, value
sources, to forecasting of future consumption of commodi- of the commodity, density, region of the country, rail car
ties, and to determining the costs of commodity shipment for ownership, state of matter (liquid, gas, and particulate), and
the purposes of mode split. type of terminal at beginning and end of the haul. Shipper
costs are added to the modal costs and represent the addi-
The report assumes that commodity production is directly tional logistics cost borne by the shipper when choosing a
related to employment in industries that produce the commod- specific carrier or mode. These costs include loss and dam-
ity. For estimating consumption, the use of an input/output age, pick up and delivery, ordering, warehousing, inventory,
(I-O) table is recommended. Commodity consumption calcu- and the possibility of running out of stock.
lations follow a three-step process: (1) obtain an I-O table, (2)
convert dollar amounts to tons and sum the columns of the table Guidebook on Statewide Travel Forecasting
to find consumption by industry, and (3) allocate tons to coun-
ties [the assumed transportation analysis zone (TAZ) size] Horowitz, A.J., "Freight Forecasting," Chap. 4, In Guide-
according to the employment by consuming industries and book on Statewide Travel Forecasting, Report FHWA-HEP-
population (for final demand) in each county. These steps 99-007, Federal Highway Administration, Washington,
embody several assumptions, which are explained. The pro- D.C., July 1999.
duction and consumption estimates can be applied to an exist-
ing commodity-flow matrix or (in the absence of a matrix) Most of this guidebook relates to passenger travel fore-
incorporated into a gravity model of shipment distribution. casting, but one chapter deals exclusively with freight fore-
Methods of forecasting industrial activity are described. casting. This chapter outlines a general method for statewide
freight forecasting and draws a distinction between statewide
For mode split, the assumption is made that all shipments and urban freight models. The chapter is organized accord-
between a pair of counties of a given commodity are allo- ing to the four steps of a standard urban transportation plan-
cated to lowest cost mode among those available between the ning model (trip generation, trip distribution, model split, and
pair. The report goes on to develop a procedure for estimat- traffic assignment) plus network development. At each step,
ing the cost of shipment by truck, railroad, and barge. No the report emphasizes the need to use existing secondary data
mention was made of air freight or intermodal. All of the cost sources. The general method has 10 steps.
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1. Obtain modal networks, is included as Appendix B in the Oklahoma Department of
2. Develop commodity groups, Transportation's Oklahoma Statewide Intermodal Transporta-
3. Relate commodity groups to industrial sectors or eco- tion Plan, Feb. 2001.
nomic indicators,
4. Find base year commodity flows, This model and forecast system were developed in 2000
5. Forecast growth in industrial sectors, by TranSystems Corporation. It is a conventional model
6. Factor commodity flows, based on Reebie TRANSEARCH data, but with a major dif-
7. Develop modal costs for commodities, ference in usability. The consultant built a calibrated truck trip
8. Split commodities to modes, model onto 31 corridor segments. The Oklahoma Department
9. Find daily vehicles from load weights and days of of Transportation (ODOT) uses a spreadsheet with the 31 cor-
operation, and ridors, upon which it can change growth factors or update
10. Assign vehicles to modal networks. truck volume. Although the spreadsheet cannot handle major
changes in the network or economy, it is a very useful tool for
A range of options is suggested for many of these steps. day-to-day forecasting.
Some specific recommendations are as follows:
For clarity, "model" refers to the TranSystems Corpora-
· Spatial unit of analysis--counties are the most conve- tion model and work, whereas "spreadsheet" refers to the
nient spatial unit within states. corridor truck forecasting system based on the model. Tran-
· Networks--network should cover all 48 contiguous Systems owns the model. ODOT uses only the spreadsheet.
states, but focus on the state of interest. Modal networks
outside of the state of interest can be adapted from sec- The TranSystems model was built to identify the major
ondary sources. freight corridors in the state. The model uses three zones
· Selection of modes--modes should be defined consis- within Oklahoma and eight zones outside the state to allocate
tent with the Commodity Flow Survey. Reebie data for externalexternal and externalinternal trip
· Selection of commodity groups--commodity groups tables. The same data identified the internalinternal trips
should be developed from Standard Transportation Com- between the three zones within Oklahoma. The study did not
modity Codes (STCCs) or Standard Classification of attempt to capture county-to-county trips or any scale finer
Transported Goods, disaggregated to the two-digit level. than the three zones.
· Trip generation--good production generation relation-
ships for commodities can be established by relating Each commodity is identified by modal split in the data.
industry output to an economic indicator for that indus-
try, such as employment. Good consumption generation The network includes only major corridors.
relationships can be developed by applying the data in
an I-O table. The model was calibrated using 19961998 ODOT truck
· Trip distribution--a gravity model is a good way of volume counts. Additional local detail was introduced by
representing commodity flows between the production segmenting the network corridors near cities.
and consumption zones. Such a model can be calibrated
to existing data, such as the Commodity Flow Survey. For example, Reebie data on I-35 show long distance
· Mode split--mode split can be handled by a number of truck movements, which can be calibrated. Then, the heavier
techniques, but the complex cost calculations of urban and suburban truck movements were picked out as part
NCHRP Report 260 should be avoided. Mode split of the calibration process. These urban and suburban areas
techniques include application of historical fixed were placed in different corridor segments, so that an urban-
shares, aggregate demand formulations, the logit rela- rural-urban corridor would be three segments; high-low-high
tion, the pivot-point relation, and elasticity methods. volume. In this way, some short trips in the corridor can be
· Traffic assignment--all-or-nothing traffic assignment indirectly modeled using the intercity data.
is recommended; however, impedances should be ad-
justed to account for biases caused by shippers defining Rail, air, and water trips are included in collected data, but
an optimal route differently from the shortest path as in- are filtered out in the modeling process. ODOT keeps statis-
dicated by traffic speeds. tics on all modes, but models only truck movement.
EXTENDED EXAMPLES OF STATEWIDE FREIGHT I-66 Southern Kentucky Corridor
FORECASTING MODELS: OTHER NOTABLE
STATEWIDE FREIGHT MODELS Wilbur Smith Associates, "Kentucky Statewide Traffic
Model Final Calibration Report," Apr. 1997.
Oklahoma Model
TranSystems Corporation, Oklahoma Statewide Intermodal Wilbur Smith Associates, "Kentucky Statewide Traffic
Transportation Plan Freight Report, Oct. 2000. This document Model Update," Jan. 2001.
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Model Structure Trip Table Calibration
This model was developed in 1997 by Wilbur Smith Associ- This model does not have a method to calibrate truck trip tables.
ates. The network and base data were updated in 2001 by
Wilbur Smith, without changing the model methodology.
Trip Assignment
The model has 1,530 traffic analysis zones; about half
in Kentucky and half in surrounding states. TAZs are based Trip assignment is based on user assumptions and desired
on groups of census tracts. The model includes TAZs up to reports. Wilbur Smith Associates describes trip assignment
3-h drive time outside Kentucky, including St. Louis, In- as ". . . the least complex part of the [model]."
dianapolis, Cincinnati, Columbus (Ohio), Nashville, and
Memphis. The Kentucky statewide model is truck only,
Growth Factors
and does not include rail, marine, or air freight.
Local truck trip growth is based on projected population and
It uses Reebie Associates data and cordon count data to employment growth in that county. A Fratar model is used to
determine truck trip generation. Future forecasts also use apply the factors.
Fratar factors.
All truck trips ends in each county are assigned to a sin- The Network
gle TAZ. This is different from automobiles, which can have
more than one TAZ per county. The network within Kentucky was developed on MINUTP.
The network outside Kentucky was developed from National
Highway Planning Network Version 2.0. The entire model
Internal Trips network was migrated to TransCAD in 2001.
Reebie Associates data are disaggregated from 56 zones
across North America, plus 28 zones in Kentucky, to 469 Updating the Model
Kentucky model TAZs (maximum of one TAZ per
county). Disaggregation is based on population and em- The model can be updated with new Reebie data, new exter-
ployment. Assumptions: equal truck trips daily (including nal distribution survey or truck volume data, and new Woods
weekends and holidays), and uniform weights of 16.8 tons & Poole population and employment data.
per truck, regardless of commodity. Reebie data assumes
that inbound and outbound trips and tonnage for each zone
are not equal, but that total inbound and outbound sums Vermont
of trips and tonnage for the entire Kentucky Model area
are equal. Cambridge Systematics, Inc., "Vermont Statewide Freight
Study," Final Report, prepared for the Vermont Department
The resulting inbound and outbound trips for each of Transportation, Montpelier, Mar. 2001.
county are not used directly, but become the baseline for the
internal trip gravity model. This gravity model determines Cambridge Systematics developed a complete freight
the internal truck trip table. forecasting model as part of the Statewide Freight Study.
This model follows a variation of the classic four-step model.
External Trips OD data included Reebie TRANSEARCH data, roadside
surveys, motor carrier surveys, and interviews with key ship-
External trips are based on cordon counts and surveys con- pers. Link data included traffic recorder and weigh-in-motion
ducted in Ohio (1996) and on traffic counts. The cordon detector, plus data from previous local and corridor studies.
counts and surveys include autos and trucks. The cordon Future commodity-flow patterns were developed by Standard
surveys show the externalexternal trips and provide the and Poor's DRI for years 2005, 2010, and 2020.
basis of distribution for the externalinternal trips. The dis-
tribution of all externalinternal trips is assumed to match The network was created from 14 in-state zones and
the survey results. All external trips are assumed to be 16 out-of-state zones. The network links and nodes are not
symmetrical, with one outbound trip matched by one in- presented in the document.
bound trip. Finally, the volume of external trips comes
from existing traffic count data at each "entry station," Annual commodity flows were converted into truck move-
where the trip enters and leaves the model. This volume ments using data from the 1997 Vehicle Inventory and Use
and distribution becomes the basis for the external Survey from the U.S. Census Bureau. The county-to-county
external and externalinternal truck trip tables. truck trip tables were built from DRI forecasts, Reebie data
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and survey data. The truck trip tables were then converted Virginia
to passenger car equivalents for assignment to the highway
network. Brogan, J., S. Brich, and M. Demetsky, "Identification and
Forecasting of Key Commodities for Virginia," Transporta-
After the highway assignment and a complete run of the tion Research Record 1790, Transportation Research Board,
model, mode split between truck and rail is determined using National Research Council, Washington, D.C., 2002, pp.
a sensitivity analysis based on the roadside surveys. Mode 7379.
split is tailored for each region (method not explained). The
resulting changes to OD tables can be compared with the The paper shows a model-building method based on only
truck-only model. major commodity flows. It also includes lessons learned
from the first step of Virginia's freight planning methodol-
Appendixes detail Reebie data, surveys and interview ogy. The freight planning methodology includes several
formats. model elements, not a complete forecasting model.
Virginia's six-step method of freight planning is:
Kansas
1. Inventory the system--the lessons learned are from
Russel, E., L. Sorenson, and R. Miller, "Microcomputer this step.
Transportation Planning Models Used to Develop Key 2. Identify the problem.
Highway Commodity Flows and to Estimate ESAL Val- 3. Establish performance measures.
ues," unpublished, prepared for the Midwest Transportation 4. Collect data for specific problems.
Center at Iowa State University and the Kansas DOT. 5. Develop and evaluate improvement alternatives.
6. Select and implement improvements
This network uses General Network Editor and Quick Re-
sponse System II (QRS II) to model the flow of five agricul- Rather than construct a model of all freight flows, the Vir-
tural commodities in Kansas. The network uses 202 zones ginia DOT purchased Reebie TRANSEARCH data and eval-
and 2,200 links. The purpose of the model is to determine uated only the 15 top commodities based on weight or value.
truck volume and axle weights (ESALs) for improved pave- Once these commodities were identified, each was assigned to
ment design. a set of OD matrices. The matrices are input to IMPLAN soft-
ware with an integral employment database, creating relation-
Data include existing K-Trans surveys and commodity ships between commodity flow, employment, and dollar
data provided by Kansas State University. The model has not value. Comparing employment, population, and other factors
been validated and is not being updated. The study team of- with commodity production and consumption, the authors
fered recommendations on improved commodity weights, used a set of regression techniques to determine production
link speeds, and other network and model changes. factors and consumption factors for each key commodity. This
way, changes in employment or related industries can be con-
Nebraska
verted into changes in tons of commodity flow. The commod-
ity flows are assigned to a statewide network, beyond the
Jones, E. and A. Sharma, "Development of Statewide Freight scope of the paper.
Forecasting Model for Nebraska" (CD-ROM), Transporta-
tion Research Board, National Research Council, Washing- No single regression technique worked well for identifying
ton, D.C., 2003. generation of consumption factors of all types of generators or
consumers on the network. Port facilities behaved very differ-
The authors use standard four-step modeling techniques ently from other types of facilities. Variables including total
for a statewide model based on the Wisconsin model, but employment and transportation employment were important
introduce a separate method for agricultural commodities factors for some commodities. Freight consumption models
based loosely on the Kansas model. Trip productions used were more accurate than freight production models. Factors
normal data sources, such as the 1993 Commodity Flow Sur- behind freight mode choice were not clear.
vey. IMPLAN software provided the I-O coefficients used to
derive trip attractions. Agricultural shipments were modeled
separately from other commodities to enable analysis of Louisiana
intermodal grain transportation. Agricultural surveys and
data sources were used to accurately determine commodity Apffel, C., J. Jayawardana, A. Ashar, K. Horn, R. McLaugh-
productions for each zone. Production, elevator locations, lin, and A. Hochstein, "Freight Components in Louisiana's
and capacity and rail service determined mode split and trips Statewide Intermodal Transportation Plan," Transportation
for each agricultural commodity. Model details and algo- Research Record 1552, Transportation Research Board, Na-
rithms are not provided. tional Research Council, Washington, D.C., 1996, pp. 3241.
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The planning procedures for the freight components of included detailed capacity measures of the different trans-
Louisiana's Statewide Intermodal Transportation Plan for fer and storage aspects of intermodal terminals for
water, rail, and intermodal components are presented. The railhighway and marine facilities, augmented by analysis
planning included U.S.DOT's four Cs (connection, choice, of the performance of terminal access routes that provide
coordination, and cooperation), as well as reflecting actual intermediate linkages to corridor and line haul routes.
freight movements.
Terminals used for capacity assessment included five
The Louisiana Department of Transportation and Devel- generic types for water and rail-based freight. The capacity
opment set a 25-year planning horizon for the study, which was determined using stock and flow analysis of terminal op-
included input from both freight users and providers. Low- erations. The study determined that capacity was the product
cost and high-capital-cost improvements were considered of two factors--effective transfer rate (tons/day) and effec-
for addressing capacity issues. These improvements were tive working time (days/year).
then evaluated by a sample of potential users and compared
with goals. Terminal access was also studied by use of a detailed in-
ventory and assessment of intermodal terminal accessibility.
A roster of statewide freight users was assembled to submit An inventory of the characteristics of local access roads and
a draft of challenges to the staff for defining statewide inter- rail spurs was made for public marine and railhighway ter-
modal needs. This statement was revised as input from con- minals in the state. Questionnaires were distributed to every
current technical analysis was presented. In addition, industry operator of these terminals in the state and were supple-
executives were interviewed to provide diverse perspectives. mented by field surveys as necessary to document the phys-
ical, institutional, and operating aspects of terminal access.
For flow analysis, four types of trips were included, internal
internal, internalexternal, externalinternal, and external
external. Historic data were gathered from the U.S. Army Corps Iowa
of Engineers (waterborne commerce statistics), Interstate
Commerce Commission (rail waybill information), and Reebie Iowa State University, "Developer's Guide for the Statewide
Associates (TRANSEARCH database). The raw data were Freight Transportation Model," Iowa State University
aggregated into business economic areas (BEAs) within the Center for Transportation Research and Education, undated
state and super BEAs for states outside Louisiana and interna- [Online]. Available: http://www.ctre.iastate.edu/Research/
tional markets. statmod/dev_guid.pdf.
Commodities were broken into 11 categories based on This TRANPLAN-based model is not a forecasting tool,
their nature, transport, handling requirements, etc. All com- but is instead used for policy analysis. As with other similar
modity movements were analyzed in terms of modal share, models, it uses Reebie TRANSEARCH commodity data, or-
origins and destinations, and domestic or foreign trade flows ganized by BEA zones, connected by TRANPLAN networks
so that factors affecting future growth could be identified and for multiple modes, and organized by standard industrial
assessed. Relational database systems were built to extract commodity codes. BEA zone data are disaggregated to
and aggregate flow measures by commodity groupings, by county level using NCHRP Report 260 techniques.
mode, and by BEA or super BEA origins and destinations.
The model uses a simple gravity model, with different fric-
Demand projections focused on three growth scenarios, tion factors for food and machinery. Only internalinternal
high, medium, and low. High and low were generated from and internalexternal trips are modeled. Externalexternal
forecasts for 19902000 made by federal agencies and in- trips are assumed to be beyond the policy applications of the
dustry groups. The growth rates were adjusted downward for model.
all commodities beyond 2000 to incorporate long-term un-
certainty. The study included three 10-year periods: The model uses separate network layers for road and rail
19902000, 20012010, and 20112020. modes. The road network is typical of other plans. The rail
network is subdivided by carrier, and interchanges between
Evaluations were made for existing and future production carriers are limited to actual interchange points defined by in-
to determine future market developments and augment earlier put from the rail carriers. Because most software is designed
information provided by static quantitative growth forecasts. for roads, the rail network noninterchange nodes were as-
The analysis included specific industrial sectors, productivity signed turn penalties. Impedance for road, rail, and inter-
trends, and the competitive position of transportation modal movements are based on cost only, not time.
providers in the state.
The model can be used for evaluation of changes in trans-
Freight network analysis focused on transshipment fa- port cost, production or consumption, and infrastructure
cilities and intermodal connections. The methodology used (network).
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SIMPLER METHODS Truck Model from the Quick Response
Freight Manual
Simpler methods are intended for rapid application of exist-
ing data to determine one or a few forecasted items. Usually Although the QRFM was intended for urban forecasts, it out-
intended for short-term forecasts, many assumptions are lines a process that might also be used to create a statewide
needed to make them work and their range of applicability is truck model. The QRFM follows a three-step process of trip
limited. generation combined with vehicle split, trip distribution, and
traffic assignment. The most interesting aspect of the QRFM
is the generation of truck origins and destinations (not pro-
ductions and attractions) for each zone by three categories of
Simpler Methods from the Guidebook
commercial vehicles: four-tire trucks, other single-unit
on Statewide Travel Forecasting
trucks, and combination trucks. Origins and destinations are
Horowitz, A.J., Guidebook on Statewide Travel Forecasting, linear functions of employment in industrial sectors and
Report FHWA-HEP-99-007, Federal Highway Administra- numbers of households.
tion, Washington, D.C., July 1999.
Elasticity Methods from NCHRP Report 388
TimeSeries Methods--The Guidebook on Statewide
Travel Forecasting discusses timeseries methods for direct Elasticity and cross-elasticity methods are suggested in
forecasts of vehicular volumes on highways and for fore- NCHRP Report 388 in the appendix, "Rail/Truck Modal Di-
casting the inputs to four-step models. Major emphasis is on version." Tables of cross-elasticities are given between rail
ARIMA (autoregressive integrated moving average) models and truck by commodity group as derived from a proprietary
and on growth factor methods. Examples are primarily for model, the Intermodal Competition Model developed by the
passenger car forecasting; however, the methods are equally Association of American Railroads. A cross-elasticity can be
applicable to truck forecasting. interpreted as the percentage change in one mode's share
given a one percent change in an attribute of another mode.
I-40 Truck Model--The Guidebook describes a linear For example, it might be possible to estimate a change in the
regression model to forecast truck volumes on I-40 in New rail share of carrying primary metals from the change in cost
Mexico. Commercial truck traffic was found to be a linear of carrying primary metals by truck.
function of the year, the U.S. disposable income, U.S.
gasoline costs, and the New Mexico cost of residential
construction. Pivot Mode Share Method from the Guidebook
on Statewide Travel Forecasting
The pivot formulation of a mode split model as found in the
Simpler Methods from the Quick Response Guidebook on Statewide Travel Forecasting was applied in
Freight Manual the Florida model discussed earlier. This method can be used
as a stand-alone technique to estimate mode shares for local-
Cambridge Systematics, et al., Quick Response Freight Man-
ized generators. A pivot formulation is able to forecast new
ual, Report DOT-T-97-10, Travel Model Improvement Pro-
mode shares from knowledge of existing mode shares and
gram, Federal Highway Administration, Washington, D.C.,
the change in a single variable in the "utility" of transporting
Sep. 1996.
a unit amount of a commodity by a particular mode. The
Guidebook recommended using cost as the single variable.
The Quick Response Freight Manual (QRFM) describes
two methods of applying growth factors to traffic volumes
that are applicable to rural highways as well as urban high- Forecasting Based on Cost Data
ways. The first method involves estimating a growth factor
from current and past truck count data and applying the re- Memmott, F.W. and R.H. Boekenkroeger, "Practical Method-
sulting factor to future years using a conventional compound ology for Freight Forecasting," Transportation Research
interest formula. The second method determines several Record 889, Transportation Research Board, National Re-
growth factors, one each for many economic indicator vari- search Council, Washington, D.C., 1982, pp. 17.
ables, usually employment in local industrial sectors. The
future growth in economic indicator variables, as calculated The freight-demand forecasting technique discussed in
by a compound interest formula, is used to forecast growth this paper is a very simple and straightforward methodology.
in commodity groups, which is then used to forecast the Compared to a formal mathematics model, the technique de-
growth in trucks carrying each group of commodities. Nec- scribed in this paper is really a process for systematically
essary assumptions about the economy and freight charac- making a large number of revenue and/or cost calculations.
teristics are discussed. A similar concept is described in The structure of the model follows these steps: (1) prepare
NCHRP Report 388. model inputs; (2) compute base case transport costs and
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revenues; (3) develop alternative futures, scenarios, and con- freight planning was in its infancy. Because many of the data
ditions; (4) compute alternative transport costs and revenues; sets no longer exist or have changed in character, the specific
(5) summarize computed information and print reports; and recommendations about them are no longer relevant. In
(6) conduct highway impact analysis. Computations for dif- addition, methods for freight planning have also changed
ferent states involve either hand or computer calculations. By substantially.
adding some other components, the structure can be modi-
fied for use in different transport models. The most impor- Still of current relevance is a matrix for each mode (rail,
tant inputs are origins and destinations of the movements or truck, ports, inland waterways, pipelines, and air cargo) that
flows and the unit costs and revenues. The paper gives some relates planning issues to data needs. The report identifies and
formats for unit costs and revenues. Two examples are de- describes 64 planning issues that could be better addressed by
scribed: one is for a Montana study and the other is for a U.S. analysis of freight data.
Army Corps of Engineers study. Control information, com-
modity flows, revenues or charges, costs, unit distances, and
vehicle equivalents are needed for study. Highway impact NCHRP Report 178
analysis is also discussed in this paper.
Roger Creighton Associates and R.L. Banks and Associates,
NCHRP Report 178: Freight Data Requirements for
Application of Regression Statewide Transportation Systems Planning: User's Manual,
and Elasticity Techniques Transportation Research Board, National Research Council,
Washington, D.C., 1977.
Morton, A.L., "A Statistical Sketch of Intercity Freight
Demand," Highway Research Record 296, Highway Research This report describes ways to implement the findings of
Board, National Research Council, Washington, D.C., 1969, NCHRP Report 177, a companion report to this one. Most of
pp. 4765. the database descriptions in this report are now obsolete;
however, the authors have provided general guidance on how
Timeseries regression analysis is used to estimate de- to use freight data in transportation planning that is still quite
mand for truck and rail. The first part of the paper describes useful. The report outlines a procedure for identifying freight
data, which were organized into five commodity groups: data requirements consisting of these steps:
agriculture, animals and products, products of forests, prod-
ucts of mines, and manufactures and miscellaneous. The rail 1. Identify Freight Issues and Problems,
and truck price index and truck rate series were needed to 2. Arrange Issues and Problems in Priority Order,
estimate the price and the cross-price elasticity of demand. 3. Establish Planning Program for Freight Transportation,
The gross national product (GNP) was used to estimate the 4. Determine Planning Methods, and
income elasticity of demand. A logarithmic form of a re- 5. Determine Data Needs and Available Resources.
gression equation was selected after three demand equations
were estimated, both logarithmically and untransformed. It The report further discusses the advantages of assem-
was assumed that one year is long enough to estimate the bling data from secondary sources and ways of obtaining
traffic volumes by using the previous year's prices. In total, primary source data. Primary source data includes traffic
12 markets were studied; two sets of equations and 324 flow data, carrier data, shipper and consignee attributes,
coefficients were estimated. For rail the growth in GNP gen- physical and operational data, and direct and indirect
erated new traffic at the level of three-fifths of the rate of impacts. A lengthy appendix provides guidance on how to
economy expansion. Economic growth generated new traffic organize and conduct a shipper survey, one of the possible
for truck at double the rate of economy expansion, and truck primary data sources.
traffic was more influenced by price.
NCHRP Report 388
OTHER RELEVANT NCHRP STUDIES
Cambridge Systematics, Inc., et al., NCHRP Report 388: A
NCHRP Report 177 Guidebook for Forecasting Freight Transportation Demand,
Transportation Research Board, National Research Council,
Roger Creighton Associates and R.L. Banks and Associates, Washington, D.C., 1997.
NCHRP Report 177: Freight Data Requirements for Statewide
Transportation Systems Planning: Research Report, Trans- NCHRP Report 388 is intended as a guidebook to help
portation Research Board, National Research Council, Wash- planners perform freight planning and forecasting. It gathers
ington, D.C., 1977. reference information about freight transportation planning
processes, techniques, tools, data, and applications. The first
This report reviewed data needs and data availability for chapter of the report describes its purpose, the characteristics
statewide freight planning during a period of time when of the freight demand, the current study, and some related
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research. The second chapter describes factors that influence · Crainic, T., M. Florian, and J.-E. Leal, "A Model for the
freight demand. The third and fourth chapters discuss de- Strategic Planning of National Freight Transportation
mand forecasting for both existing and new facilities. New by Rail," Transportation Science, Vol. 24, No. 1, 1990,
facility options include new highways for serving rail yards, pp. 124.
new rail facilities for current railroads, new rail facilities for · Stevens, B., Basic Regional Input-Output for Trans-
competing railroads, and new U.S. or foreign port terminals. portation Impact Analysis, NCHRP Project 8-15A,
The last chapter describes policy analysis. Regional Science Research Institute, Philadelphia,
Pa., July 1982.
The report's appendixes contain a wealth of useful infor- · Eusebio, V. and S. Rindom, Grain Transportation Ser-
mation; factors influencing freight demand, reviews of vice Demand Projections for Kansas: 1995 and Beyond,
freight-demand forecasting studies, freight activity data Kansas Department of Transportation, Topeka, July
sources, freight transportation survey procedures and meth- 1990.
ods, statistical forecasting techniques, estimating transport
costs, rail and truck modal diversion, three modal-diversion Some of these reports and articles are also reviewed here.
models, case studies, and the information needs perceived by
public agencies. Appendix C contains detailed descriptions of three dozen
data sources related to freight transport activity and demand.
Appendix B is an annotated bibliography of several of the These include:
more important freight-demand studies.
· 1993 Commodity Flow Survey;
· Cambridge Systematics, Inc., Alternative Planning Ap- · TRANSEARCH;
proaches: Structural and Direct, NCHRP Project 20-17, · Freight Transportation and Logistics Service;
Statewide Freight Demand Forecasting, May 1980. · U.S. Exports by State of Origin of Movement;
· Memmott, F. and Roger Creighton Associates, NCHRP · Directory of U.S. Importers and Exporters;
Report 260: Application of Freight Demand Forecasting · National Transportation Statistics, Annual Report;
Techniques, Transportation Research Board, National · Freight Commodity Statistics;
Research Council, Washington, D.C., 1983. · North American Trucking Survey;
· Memmott, F.W. and R.H. Boekroeger, "Practical · LTL Commodity and Market Flow Database;
Methodology for Freight Forecasting," Transportation · Nationwide Truck Activity and Commodity Survey;
Research Record 889, Transportation Research Board, · Ship Movement Database;
National Research Council, Washington, D.C., 1982, · Truck Inventory and Use Survey;
pp. 17. · State Estimate of Truck Traffic;
· Kim, T.J. and J.J. Hinkle, "Model for Statewide · Quarterly Coal Report;
Freight Transportation Planning," Transportation · Natural Gas Annual;
Research Record 889, Transportation Research · Surface Transborder Trade-Flow Data; and
Board, National Research Council, Washington, · Port Import and Export Reporting Service.
D.C., 1982, pp. 1519.
· Middendorf, D.P., M. Jelavich, and R.H. Ellis, "Devel-
opment and Application of Statewide, Multimodal NCHRP Synthesis 298
Freight Forecasting Procedures for Florida," Trans-
portation Research Record 889, Transportation Re- Fischer, M.J. and M. Han, NCHRP Synthesis of Highway
search Board, National Research Council, Washington, Practice 298: Truck Trip Generation Data, Transportation
D.C., 1982, pp. 714. Research Board, National Research Council, Washington,
· Hu, P., T. Wright, S. Miaou, D. Beal, and S. Davis, Es- D.C., 2001, 81 pp.
timating Commercial Truck VMT of Interstate Motor
Carriers: Data Evaluation, Oak Ridge National Labo- This report is essential. It describes vital data sources, key
ratory Report, Oak Ridge, Tenn., Nov. 1989, 176 pp. considerations for forecasting, many best-practices tech-
· Friedlaender, A.F. and R.H. Spady, "A Derived Demand niques, and many common mistakes made in planning and
Function for Freight Transportation," Review of Eco- modeling.
nomics and Statistics, Vol. 62, No. 3, 1980, pp. 432441.
· Lawrence, M.B. and R.G. Sharp, "Freight Transporta- Chapter two discusses how truck productions and attrac-
tion Productivity in the 1980s: A Retrospective," Jour- tions differ from automobile trips and activities. It also in-
nal of the Transportation Research Forum, Vol. 32, No. cludes a discussion of data collection techniques.
1, 1991, pp. 158171.
· Winston, C., "The Demand for Freight Transportation: Chapter three is an annotated bibliography of data
Models and Applications," Transportation Research, sources, organized by topic. Topics include compendia of
Vol. 17 (A), 1983, pp. 419427. trip generation data, engineering studies, special generator
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studies, ports and intermodal data sources, vehicle-based NEAC
travel demand models, commodity-based travel demand
models, and other critical data resources. The NEAC-Model, "The Solution to Western and Central Eu-
ropean Transport Information Problems! Base Year 1997--
Chapter four describes the current (2000) state of the art. A Forecast 2020" [Online]. Available: http://www.nea.nl/dutch/
key lack of uniformity in statewide vehicle-based forecasting publicaties/Brochures/NEAC-folder.pdf.
is the discrepancy between linked (truck with multiple stops)
and garage-based (truck with single destination) truck trips. NEAC is a decision support system covering all of Europe
Metropolitan and statewide models treat each differently, that provides the link between traffic and economic develop-
making comparisons or pattern identification difficult. ment in and between regions. The advantage of NEAC's
Vehicle-based models as a function of employment are popu- database is that it can determine the exact origin and desti-
lar within metropolitan models. Commodity-based models are nation of commodities in region-to-region transport as well
more popular at the statewide level and have different prob- as the organization of transport (direct or with transship-
lems. Errors in commodity payload factors and other assump- ment). Information on the route of shipment can be provided.
tions are the most common. The NEAC transport chain database can help analysis of
transport flows and forecasting based on economical rela-
The report does not recommend methods to standardize tions. The concept of the transportation chain is described as
or share data among different models (and different orga- follows: It is a sequence of transport modes used to carry a
nizations), and calls for additional research mostly on im- certain good from its first origin to its final destination.
proved data collection. There is no significant discussion Along the chain, one or more transshipments take place.
of alternative or lower cost data, new mathematical NEAC has been applied in some European regions on a range
or computer tools to improve the process or changes, or of topics including transport flow analysis, corridor analysis,
alternatives to the basic process of trip generationtrip infrastructure analysis, market potential analysis, and policy
distributiontraffic assignment. The report does not impact analysis.
address intermodal activities beyond bibliographic refer-
ences to special generator models.
EUFRAT
PRODEC Resources, "The European Freight Assessment
ACTIVE NATIONAL OR INTERNATIONAL MODELS
AND TOOLS Model" [Online]. Available: http://www.prodec.dk/resources/
eufrat/eufrat.htm.
Freight Analysis Framework
"The Development of the Freight Analysis Framework Data- EUFRAT is a multimodal freight assessment model. The
base and Forecast," no date (around 2003), Booz, Allen and network includes all major road, rail, inland waterway, and
Hamilton [Online]. Available: http://www.ops.fhwa.dot.gov/ sea connections, which covers all of the European Union, the
freight/lambert_files/CombinedFinalMethodologyPiece-2.doc. European Free Trade Association countries, and all of East-
ern Europe, including Russia and Ukraine. It has been ap-
Fekpe, E., M. Alam, T. Foody, and D. Gopalakrishna, plied with reported good results. It uses the common Euro-
"Freight Analysis Framework Highway Capacity Analysis, pean standard for regional statistics (NUTS2, sometimes
Draft Methodology Report," Battelle, Apr. 18, 2002 NUTS3) and the freight volumes are based on the OECD
[Online]. Available: http://www.ops.fhwa.dot.gov/freight/ SITC Rev. 2 commodity classes.
lambert_files/Capacity-Method-report-revised.doc.
The FAF is a freight forecasting model, developed by SAMGODS
FHWA, which covers the contiguous 48 states and the Dis-
trict of Columbia. FAF employs the general methodology of "National Freight Model System for Sweden" [Online].
OD table factoring and assignment to perform forecasts to Available: http://www.rand.org/publications/MR/MR1663/
2010 and 2020 from base year OD tables of 1998. MR1663.pdf.
SAMGODS is a freight model for Sweden that is cur-
GBFM rently under development.
"Great Britain Freight Model" [Online]. Available:
http://www.mdst.co.uk/MDSTBody-gbfm.htm. GTAP
GBFM is an offshoot of STEMM (Strategic European "Global Trade Analysis Project (GTAP)," Purdue University,
Multimodal Modelling) and contains specific improvements West Lafayette, Ind. [Online]. Available: http://www.gtap.
for applications in Great Britain. agecon.purdue.edu.
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Hertel, T. and T. Marinos, "Structure of GTAP," Draft input varies based on the firm's location in the global (or re-
of Chapter 2, In Global Trade Analysis: Modeling and gional) economy, the skills base, and the readiness of substi-
Applications, Cambridge University Press, 1997 [Online]. tutes. For example, firms in developed nations tend to use
Available: http://www.gtap.agecon.purdue.edu/resources/ capital-intensive, technologically sophisticated production
download/86.pdf. that minimizes land, labor, and intermediate input costs. De-
termining rates of substitution of resources is a major part of
The Global Trade Analysis Project (GTAP) is a complex modeling firm behavior; for example, purchasing more effi-
set of databases and sophisticated economic modeling tools cient equipment when fuel prices rise. Another example is
developed by an international consortium of universities, in- that intermediate inputs may be locally produced or pur-
stitutions, and government departments in the developed chased internationally. Finally, each sector of the economy
world. The consortium began work in 1993, and research and (agriculture, machine manufacture, and banking) has its own
development work is ongoing. special resource needs.
The international consortium approach to developing the Households input government services, income, and goods,
model and framework has led to wide use of GTAP economic and output taxes, purchasing, and savings. Purchase rates by
models for policymaking in the World Trade Organization industrial sector are based on birth rates and other criteria.
(WTO), World Bank, and several international conferences. Changes in purchasing are based on elasticities of demand
(goods prices), taxes, population change, and other factors.
The model uses 57 commodity sectors, some of which do
not need freight transport (banking, electricity), but are still The GTAP world also includes mechanisms for infla-
considered commodities for purposes of the economic tion and the distribution of multiregional (or transnational)
model. Charts show the mapping of these sectors to U.S. investments.
STCC groups. Only three types of industrial sectors are iden-
tified: agriculture and food, energy, and goods and services. Regions exchange value using the model's Global Bank
The Earth is divided into 66 regions for determining trade and Global Trade mechanisms. Both are monitored only in
between them. terms of value. Global Trade does include commodities, but
by value instead of tonnage.
The main uses are to model the effects of economic
growth, trade policy changes, and impacts of changes in re- For use in statewide freight forecasting, GTAP has limited
sources, technology, and the environment. usefulness. The model can provide predicted rates of economic
growth within the given regulatory and tariff framework, but
The model structure uses generally accepted market-based converting such models into economic forecasts is already done
economic principles, in most cases, to determine number by the U.S. Department of Commerce and other organizations.
value (as opposed to real quantity or dollar) relationships Specifically for states with major international port or border
between producers, consumers, and governments. The set of activities, GTAP can provide useful commodity value forecasts
interlocking relationships causes value to flow and the econ- that can be converted into tonnages.
omy to change or grow, as it would in a real economy. The
dynamic flow of value is monitored and held in equilibrium GTAP use as a long-term predictive model is unknown.
by a layer of accounting within the model. For example, the Its methodology is complicated, but not controversial. It has
change in price of commodity X is determined not just by sup- not seen major use as a predictive model, and has been in use
ply and demand, but also by weighted averages of the costs of for only 10 years.
related commodities and possible substitutable commodities.
Tax structures and import and export taxes are included.
STEMM
Because the model uses market-based economic behavior
predictions, government fiscal and revenue policy is highly "STEMM Final Summary Report" [Online]. Available:
discretionary. Government is also immune to many of the ac- http://www.cordis.lu/transport/src/stemmrep.htm, last up-
counting checks. Balanced budgets are neither assumed nor dated April 1999.
required in the GTAP universe. Similarly, the value flows
and accounting levels are assumed to be transparent to STEMM (Strategic European Multimodal Modelling) has
macroeconomic policies, monetary policy changes, and other a multimodal freight model that has flow attributes, includ-
non-market-driven events. The feedback from market to pol- ing disaggregation levels and mode and route choice algo-
icy is political, not economic, and no adequate modeling rithms. For each mode and route a generalized cost is calcu-
mechanism exists. lated by adding financial cost and various qualities of service
penalties. Only alternatives within a certain percentage of the
Firms purchase land, labor, capital, intermediate inputs, lowest generalized costs are considered and if they are the
and knowledge (technology) to produce their outputs. Each same, then traffic will be split between them.
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The principle output of the model is a set of region-to- identify various commodity groups and historical data.
region commodity flows that can be used in the planning The study also contains a detailed analysis to compute a
process, and additional outputs include regional economic series of timeseries equations for straight line, second-
activity, national income, and value-added. degree curve, exponential curve, and second-degree expo-
nential curve. Owing to multicollinearity problems with
The INSA model does not use a separate mode split the original 15 variables, 5 runs were made with different
model, but combines a modal share and network routing combinations of variables that limited the problems. After
analysis. It also uses a circuity constraint of an ellipse of running mathematical methods to obtain forecasts, non-
given eccentricity being constructed about the origin and mathematical procedures based on judgment and the
destination regions for a particular commodity movement. In knowledge of analysts was used. The study was very basic
addition, an optional inertia effect may be used to constrain in nature.
a specified portion of any commodity shipment to observe
modal-share percentages input by the user for that shipment.
Intermodal
Hawnn, A.F. and F.M. Sharp, "Inland Navigation Sys-
tems Analysis," Transportation Research Record 636, Intermodal Demand in Arkansas
Transportation Research Board, National Research Council,
Washington, D.C., 1977, pp. 1422. Ozment, J., "Demand for Intermodal Transportation in
Arkansas," Walton College of Business, University of
The purpose of the INSA commodity-flow model is to Arkansas, Fayetteville, unpublished paper (undated, around
forecast the demand for interregional bulk commodity trans- 2001).
portation. The model is an I-O model in which market dy-
namics determine the location, composition, and pricing of In this white paper, the author asserts that the demand for
output, and the behavior of economic aggregates determines truck and rail intermodal services in Arkansas is the result of
the level of output. ineffective public policy relating to intermodal and misper-
ceptions of traffic managers as to the cost advantages of in-
The model uses economic inputs such as economic activ- termodal. The author states that application of conventional
ities, regional attributes for 173 BEA areas, demand, and logistics theory would suggest many additional opportunities
transportation costs. Operations in the model are determina- for intermodal shipping, especially in commodities of low
tion of minimum cost and location, computation of value per weight. The analysis applied a series of cost as-
consumption, determination of demand, organization of sumptions to a variety of specific commodities (not com-
transportation costs, forecast of economic activity, and modity categories). The computed total logistics cost was
allocation of commodity flow. Outputs include a commodity- composed of
flow report, a domestic-demand report, and an origin-flow
report. Total Cost = OC + CC + Tr + PC + It + SS + Other (D5)
The operations of the path-selection algorithm yield iden- where
tification, number of tons assigned, shipping costs for each OC = order placement cost,
commodity shipment, and shipping costs and transit time of CC = inventory carrying cost,
assigned traffic. An ellipse about the origin and destination Tr = transportation cost,
is used to reduce the number of paths, and commodities may PC = product cost,
also be restricted as to which modes of transportation they It = inventory in transit cost, and
may use. INSA also includes an optional inertia effect, and SS = safety stock cost.
an iterative procedure is used to assign shipments to the
network. and where
OC = A(R/Q),
TimeSeries Analysis CC = 1/2(QVW),
Tr = rRwt/100,
Branyan, C.O. and G.D. Mickle, "Projecting Commodity PC = VR,
Movements for Inland Waterways Port Development," It = iVRt/365, and
Transportation Research Record 669, Transportation Re- SS = BVW.
search Board, National Research Council, Washington, D.C.,
1978, pp. 57. and where
This study contains a preliminary analysis including Q = optimal order quantity (EOQ), Q (2AR/VW)1/2,
gathering information from the BEA and local agencies to A = cost of placing an order,
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R = annual rate of use, market changes. It has limited use for models in agricultural
V = value per unit, areas, because it reflects opinion only and no specific data or
W = carrying cost as a percentage of average value of analysis are presented. Consensus is that light-duty track
inventory, mileage and car fleet size will decrease owing to low growth
r = transportation rate per 100 pounds (CWT), and increasing efficiency. Mileage losses will occur as
wt = weight per unit, heavier-duty mainline track, which can carry heavier cars,
i = interest rate or cost of capital, will cause elevator expansion along main routes and elimi-
t = lead time in days, and nate market areas of smaller elevators on other routes.
B = buffer of inventory to prevent stockouts.
Thus, Empties
Comparison of Methods
TC = A(R/Q) + 1/2(QVW) + rRwt/100
+ VR + iVRt/365 + BVW (D6) Holguin-Veras, J. and E. Thorson, "Practical Implications of
Modeling Commercial Vehicle Empty Trips" (CD-ROM),
Original Source: Coyle, J.J., E.J. Bardi, and C.J. Langley, Jr., Presented at the 82nd Annual Meeting of the Transportation
The Management of Business Logistics, 6th ed., West Pub- Research Board, Jan. 1216, 2003.
lishing, St. Paul, Minn., 1996 and Ballou, R.H., Business
Logistics Management, 4th ed., Prentice Hall, Upper Saddle The paper compares four methods of modeling empty
River, N.J., 2004. truck trips for feedback into OD tables. Empty trips represent
30% to 50% of freight trips. Each method is used in a two-
zone simulation and in a 26-zone simulation based on data
COMMODITY STUDIES
from New York City. All methods produced undercounts of
Agricultural the total number of truck trips, but the HolguinVeras and
Thorson (HVT5) and Noortman and van Es (NVE) methods
Effect of Unit Trains produced the smallest errors, approximately 5% to 6% of to-
tal observed trips.
Linsenmeyer, D., "Effect of Unit-Train Grain Shipments on
Rural Nebraska Roads," Transportation Research Record
875, Transportation Research Board, National Research Hazardous Materials
Council, Washington, D.C., 1982, pp. 6064.
Erkut, E. and T. Glickman, "Minimax Population Exposure
This paper explores the effect of a change in market area in Routing Highway Shipments of Hazardous Materials,"
and effect on truck ton-miles by a change in rail operating Transportation Research Record 1602, Transportation Re-
practice. Truck ton-miles increased 71%, profitable length- search Board, National Research Council, Washington, D.C.,
of-haul increased by 815 mi, and heavier trucks became 1997, pp. 93100.
more profitable as a result of switching from single-rail car-
loads to unit-trains, with an accompanying concentration of This paper uses a two-step routing method for hazardous
grain elevators. truck shipments. First it sets a constraint criterion, such as
population along a network link. Any links exceeding the cri-
The paper provides a clear methodology for relationships terion are excluded from the second step. The second step is
between agricultural production and different modes, but the a typical shortest path or minimum impedance routing algo-
relationships are not applicable outside of this case. Not di- rithm. The larger implications or applications to modeling or
rectly useful for modelers, the paper does provide a good ex- forecasting are not explored. Applications to oversize, over-
ample of unintended effects. weight, or other constrained trucks are not explored.
Coutinho-Rodrigues, J., J. Current, J. Climaco, and
Shipper Expectations of Rail S. Ratick, "Interactive Spacial Decision-Support Systems for
Multiobjective Hazardous Materials Location-Routing Prob-
Vachel, K. and J. Bitzen, "Long-Term Availability of Rail- lems," Transportation Research Record 1602, Transporta-
road Services for U.S. Agriculture," Transportation Re- tion Research Board, National Research Council, Washing-
search Record 1790, Transportation Research Board, ton, D.C., 1997, pp. 101109.
National Research Council, Washington, D.C., 2002, pp.
6672. The authors created a computer application, ISDSS, to
model hazardous material flows, including production or
This study is a survey of the expectations of Midwest generation, transport, use, and disposal or processing. The
grain shippers and railways on the effect of technology and model is oriented toward risk management, not transport.
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The application optimizes flows and locations based on user- The data from both shippers and consignees were com-
defined networks and weighted criteria. The model algo- bined, and the five explanatory variables for modal choice
rithms are not discussed. This is not routing or forecasting considered in the study were average shipment size, loads
software, but solution software for hazardous material risk (full or less than full), hire (private or for hire), and control.
management. The study only uses the truck and rail modes, because all
other modes carry very little freight.
Chang, T., L. Nozick, and M. Turnquist, "Routing Haz-
ardous Materials with Stochastic, Dynamic Link Attributes: For the log-linear model different combinations of load
A Case Study" (CD-ROM), Transportation Research Board, (L, i = 1,2), hire (H, j = 1,2), and mode (M, k = 1,2) were con-
National Research Council, Washington, D.C., 2002. sidered. For example, a log-linear model [LH][MH] is a
model that includes the association of "loads" and "hire" in-
The authors describe a multi-objective routing algorithm dividually with "modes." Two cases yielded a likelihood
with case study. The label-correcting network algorithm uses ratio less than one. After testing the two for statistical signif-
a convolution-propagation approach, based on the algo- icance and finding the chi-squared value, the model with
rithm's ability to "test" different paths and rule out some fewest variables was chosen because there was no substan-
early. The multiple objectives are incorporated using proba- tial difference. The saturated model chosen was [LM][HM]
bilities, including hazardous material release probabilities and is expressed as
and driver and population exposure probabilities. The algo-
rithm includes variables for congestion and time of day to de- ln mijk = + L(i) + H(j) + M(k) + LM(ik) + HM(jk) (D7)
termine different routes. The algorithm is clearly presented.
The example network is summarized, and only a single route
is determined. The second part of the study was to construct a logit
model to develop a table of log odds to understand how
Patel, M.H. and A.J. Horowitz, "Optimal Routing of changes in the combined levels of explanatory variables
Hazardous Materials Considering Risk of Spill," Trans- affect the response variable. The logit equation that was used
portation Research A, Vol. 28A, No. 2, Mar. 1994, pp. is defined as
119132.
logitij = 2[M(1) + LM(i1) + HM(j1)] (D8)
The authors propose an algorithm for routing hazmat that
minimizes the risk of population exposure to airborne toxic Odds ratios then were calculated and proportions were found
substances that might be released in a crash. using the transformation proposed by Berkson in 1944 by
mode, load, and hire for different commodities.
ADVANCED METHODS STUDIES
Mode Split Mode Choice Factors
Log-Linear and Logit Models Wilson, F.R., B.G. Bisson, and K.B. Kobia, "Factors That
Determine Mode Choice in the Transportation of General
Murthy, A.S.N. and B. Ashtakala, "Modal Split Analysis Freight," Transportation Research Record 1061, Trans-
Using Logit Models," Journal of Transportation Engineer- portation Research Board, National Research Council,
ing, Vol. 113, No. 5, 1987, pp. 502519. Washington, D.C., 1986, pp. 2631.
The study includes the analysis of survey and question- This study examines the factors that shippers in eastern
naire data collected in Alberta, Canada, from shippers and Canada use to determine modes of freight shipments by hired
consignees. Log-linear and logit models were then used truck, private truck, and rail. A survey was used instead of
to create a more statistically credible and comprehen- gathering waybill data, because waybill data do not include
sive method to identify the dominant modes of commodity level of service attributes and differences in record keeping,
movement. and many shippers consider waybill data to be proprietary.
Communities were classified as shippers (sources), con- This study classifies four factors for mode choice, includ-
signees (sinks), or both. Major commodity-flow data such as ing characteristics of the transportation system, characteris-
type, mode, loads (full or less than full), control, hire (private tics of the shipment, characteristics of the local carriers, and
or for-hire), and market share were gathered, as well as de- characteristics of the shipper.
mographic data such as population, retail sales volumes, etc.,
and other data from transportation and government agencies. Analysis was performed using three linear logit models.
Of the surveys gathered, 1,318 of the responses were ship- The difference between the first two is that one considers in-
pers and 6,175 were consignees. transit damage and one considers commodity value because
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both cannot be used in the same model owing to multi- The model results are compared to a normal four-step grav-
collinearity problems. The third model uses derived variables ity model process. The fractionalsplit and gravity models
instead of specified variables. were in close agreement. The advantages and disadvantages
of each method are not clearly discussed.
The data showed increasing use of rail as length of transit
increases. Shipping cost, in-transit damage, and commodity
value were not significant in influencing mode choice. Sig- Gravity Model
nificant influences for both rail and private truck modes were
not covered in this survey. Data suggest that model data Black, W.R., "The Utility of the Gravity Model and Estimates
should be gathered using personal interview data, which pro- of Its Parameters in Commodity Flow Studies," Proceedings
vide a higher level of accuracy and could be used to explore of the Association of American Geographers, Vol. 3, 1971,
other factors not covered in a survey. pp. 2832.
The paper reports and evaluates the results obtained from
Flows applying the gravity model to 24 sets of interregional com-
modity flows for the United States in 1967. The study uses a
Accuracy Study
variation of the gravity model, substituting shipments and
Metaxatos, P., "Accuracy of Origin-Destination Highway demands for productions and attractions.
Freight Weight and Value Flows" (CD-ROM), Presented at
the 82nd Annual Meeting of the Transportation Research The total shipments and demands are assumed known for
Board, Jan. 1216, 2003. each region and represent the row and column sums for a
commodity-flow matrix. The only unknown term used in the
This paper presents a method of estimating interstate or study was the friction factor coefficient and it was increased
international (externalinternal) freight flows using matrices by 0.025 in a stepwise procedure until the correlation be-
and commodity data similar to common internalinternal OD tween the actual and estimated flows failed to increase.
tables. The external side of the commodity flow is repre-
sented by a single data source, a seaport or border crossing. The study used high regional generalization in the flows
The internal side of the commodity flow is represented at the reported between the nine census regions and 81 possible
county level using existing disaggregation. A set of OD ma- inter- and intraregional flows. The interregional distance was
trices uses a gravity model to simulate long-distance freight defined as half the square root of the region's area.
flows and determine value or weight. Results are determined
by the number of iterations of the gravity model, which is The estimates obtained from the gravity-type trade model
governed by the desired confidence level. The paper does not for the 24 shipper groups were quite accurate and the model
explore possible expansion of the technique to external accounted for 93% of the variance in the flows examined.
external or internalinternal freight flows or to distributed Overall, the study suggests that it is clearly possible to esti-
external sources. No example problem or comparison to an mate reliable friction factors.
existing gravity model is provided.
Ashtakala, B. and A.S.N. Murthy, "Optimized Gravity
Models for Commodity Transportation," Journal of Trans-
Disaggregation portation Engineering, Vol. 114, No. 4, 1988, pp. 393409.
Sivakumar, A. and C. Bhat, "Fractional Split-Distribution The objective of the study was to reexamine survey data
Model for Statewide Commodity-Flow Analysis," Trans- (Murthy and Ashtakala 1987) and develop models for com-
portation Research Record 1790, Transportation Research modity transportation. A gravity model with a new technique
Board, National Research Council, Washington, D.C., 2002, for calibration is proposed.
pp. 8088.
The commodity data were classified and survey data
A variation of the four-step modeling process, the authors about shippers and consignees were gathered. The data col-
create a Texas model using fractionalsplit distribution. This lected were origin and destination of commodity movement,
distribution uses fractions to determine origins and destina- type of commodity, type of firm, annual tonnage, average
tions. For example, zone A produces 1 good, and zone B con- shipment size, type of load (full or less than full), type of hire
sumes 1/10th of the good. Zone B also consumes 1/20 of the (private or for-hire), control (yes or no), and market share.
same good produced at Zone C. Demographic data were also gathered.
The data were from the Reebie TRANSEARCH database. OD tables were developed showing origins (sources) and
Only three commodity groups were used. Beyond the struc- destinations (sinks). A series of production-constrained grav-
ture of fractionalsplit methodology, the model is not shown. ity models were then applied to the data from source to sink
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110
and compared differences in interchanges using regression Freight internal productions consisted of the 1993 Com-
analysis. The gravity model with the highest R2 value was modity Flow Survey, employment for each zone and
used as the best representation of real data. The spatial sepa- economic sector (U.S. Census County Business Patterns),
ration factor is specific to each commodity category, so there population for each zone (U.S. Census), and tons of com-
is one gravity model for each category. The models are modity per truck for each STCC (Reebie Associates). Each
shown by statistical measures and commodity haul fre- commodity is disaggregated into STCC, zone, and internal-
quency diagrams to be acceptable. to-internal or internal-to-external trip type. Truckloads were
assumed to be uniform across 6 days each week (312 days
per year). The final freight productions were a series of 624
Trip Length tables showing tons produced by each of 28 STCC sectors at
each of the 624 network TAZs in Wisconsin. Productions for
Holguin-Veras, J. and E. Thorson, "Trip Length Distributions external zones were not considered.
in Commodity-Based and Trip-Based Freight Demand Model-
ing Investigation of Relationships," Transportation Research Freight internal attractions were determined using I-O co-
Record 1707, Transportation Research Board, National Re- efficients. From the IMPLAN software package and 1994
search Council, Washington, D.C., 2002, pp. 3748. Wisconsin data (source not cited) and IO coefficients (source
not cited), the monetary amount of one product needed by
The trip length distribution (TLD) in freight-demand mod- each industry to produce its output were summed for each of
eling can be defined as either a tonnage TLD or vehicle TLD the 28 sectors used, resulting in a statewide estimate of total
for different models. The main aim of this paper is to exam internal freight attraction volume. The total was disaggre-
the characteristics of the tonnage TLDs and vehicle TLDs to gated by employment (U.S. Census County Business Pat-
find the relationship between the two and to identify problems terns) to the TAZ level. If no reliable employment numbers
when using TLDs. The shape of a TLD will be different were available, population was used for disaggregation.
within different environments in which freight movements
take place. Major generators have a significant impact on the Freight external attractions (imports) were based on the
shape of a TLD. If a mathematical relationship between the IMPLAN final-demand report. Demand was disaggregated
two types of TLDs can be found, it will help exploit the best to the TAZ level using employment and/or population.
features of commodity-based and truck-based models. IMPLAN regional purchase coefficients (source not cited)
determined the amount of final demand allocated to internal
and external supply.
InputOutput
Trip distribution was done by the gravity model function
Application of I-O for Commodity Flows of TRANPLAN. Traffic assignment was mentioned, but not
discussed.
Sorratini, J.A., "Estimating Statewide Truck Trips Using
Commodity Flows and Input-Output Coefficients," Journal The model was calibrated to 40 selected links. Root mean
of Transportation and Statistics, Vol. 3, No. 1, Apr. 2000, pp. square error (RMSE) of predicted flows against actual counts
5367. (Wisconsin DOT) ranged from 32% to 61% under different
conditions of complexity. Several iterations of the gravity
Sorratini, J.A. and R.L. Smith, Jr., "Development of a model changed the RMSE range from 27% to 57%. The
Statewide Truck Trip Forecasting Model Based on Com- highest errors were in the lower volumes. Volumes of more
modity Flows and Input-Output Coefficients," Transporta- than 2,000 vehicles and three or more gravity model itera-
tion Research Record 1707, Transportation Research Board, tions had an RMSE of 27%.
National Research Council, Washington, D.C., 2002, pp.
3748. Vilain, P., L. Liu, and D. Aimen, "Estimation of Commod-
ity Inflows to a Substate Region," Transportation Research
This study used inexpensive data, the 1993 Commodity Record 1653, Transportation Research Board, National
Flow Survey and I-O coefficients to create freight trip gen- Research Council, Washington, D.C., 1999, pp. 1726.
eration tables. The resulting tables were used in a standard
four-step modeling process. Results were generally within This is a method of developing external-internal trip ta-
25% of traffic counts. These papers deal only with freight bles using existing I-O data and commodity-flow data
highway flows. instead of cordon counts and surveys. The article includes a
calibrated example. The sum of estimated commodity flows
The network was made of 72 internal zones within Wis- calculated from I-O tables in the example was within 6.6%
consin, plus 70 external zones. Zones matched the Reebie of the observed value shown in the 1993 Commodity Flow
Associates TRANSEARCH data used for freight produc- Survey. Weighted average error for individual commodities
tions. Network characteristics were not discussed. was up to 28%.
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111
Data required are I-O tables from the U.S. Department of tains these major elements: trip distribution with a doubly con-
Commerce, Bureau of Economic Analysis, and commodity- strained gravity model; a shipper mode choice and route
flow surveys from the U.S. Department of Commerce, choice process; a way of allocating shipments to carrier net-
Bureau of Census. The advantages of this method are that the works; and a carrier route choice process. Separate, but com-
data collection is easy, including data for calibration, and that patible, networks are used to model the shipper and carrier
the method can be set up on existing spreadsheet applica- routing decisions. The shipper routing decision process is
tions. Disadvantages include methodological assumptions based on an elastic-demand user-optimal equilibrium assign-
and limitations that can lead to significant errors in some ment, where as the carrier routing decision is a fixed-demand
commodity groups. system optimal over the carrier's subnetwork. These shipper
and carrier decision steps are sequential (without feedback).
The method, using matrix algebra, is to convert the I-O The carrier choice networks provide for movements across
table into a supply-side commodity-flow matrix. Then the carriers, backhauling, and delays along mainlines and in yards.
method determines the location quotient (percent of regional Both link cost and link traversal time are used in the route
employment divided by percent of total U.S. employment) choice process. Tests were conducted on networks with up to
for each industry. Commodities and industries are 38 stan- 15 commodity groups and up to 15,000 single-direction links
dard types used by the U.S. Department of Commerce for I-O (arcs). The unit of spatial aggregations was a BEA region.
tables. The final result is external-to-regional commodity
flows for each industry and commodity type. The possibility
of using areas smaller than regional (such as county or TAZ Hypernetworks
level) was not explored.
Friesz, T.L. and E.K. Morlok, "Recent Advances in Network
Two important assumptions are that commodities used by Modeling and Their Implications for Freight Systems Plan-
each industry are identical nationally, and cannot vary from ning," Transportation Research Forum Proceedings, 1980,
region to region, and that the fraction of each commodity pp. 513520.
purchased locally is identical across all industries. For ex-
ample, industry A uses four tons of commodity N input for This paper reports on initial efforts at building freight
each ton of output, regardless of the local price or availabil- forecasting models that are superceded by later work by the
ity or possibility of substitution. Furthermore, if 10% of all same authors. The intent of the paper is to draw a distinction
of the state's use of commodity N is produced internally, then between passenger models and freight models. The authors
industry A will purchase 10% of its N locally, regardless of concentrate on traffic assignment and show how transship-
production, transportation, regulatory, or other impacts. ment can be accommodated with a hypernetwork. The paper
makes two contributions: (1) it shows how user-optimal
In the example problem, the sum of commodity flows was equilibrium assignments may be accomplished with multiple
measured against the 1993 Commodity Flow Survey bench- classes; and (2) it demonstrates that carriers can choose their
marks. The sum of all commodities was overreported by own criteria for optimizing their paths.
6.6% of actual flow, with a weighted mean average of 28%
error for individual commodities. Mineral products and
petroleum and coal products predictions were particularly State of the Art, Early 1980s
poor, and removing them changed the sum of commodities
Friesz, T.L., R.L. Tobin, and P.T. Harker, "Predictive Inter-
underreported by 9% with a weighted mean average of 17%
city Freight Network Models: The State of the Art," Trans-
for individual commodities.
portation Research A, Vol. 17A, No. 6, 1983, pp. 409417.
Final matrix and table results from this method are yearly
The authors review several similar approaches to intercity
flows. Breakdowns by day or time-of-day were outside the
freight forecasting, with an emphasis on their own work.
scope of study.
They concentrate on routing decisions within macroscopic
equilibrium network frameworks. Although the authors men-
Networks and Traffic Assignment tion heuristic attempts to solve complex problems of shipper
and carrier behavior, they are much more interested in algo-
Sequential Shipper-Carrier Networks rithms based on optimization theory or variational inequality
theory and how these models have developed incrementally
Friesz, T.L., J.A. Gottfried, and E.K. Morlok, "A Sequential as additional theory is added to what has already been done.
Shipper-Carrier Network Model for Predicting Freight Flows," They critique six full-scale models in terms of 16 attributes:
Transportation Science, Vol. 20, No. 2, 1986, pp. 8091.
· Treatment of multiple modes,
The authors report on the development of three similar in- · Treatment of multiple commodities,
tercity freight models that have their greatest emphasis on ob- · Sequential loading of commodities,
taining accurate traffic assignments. Each of the models con- · Simultaneous loading of commodities,
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112
· Treatment of congestion phenomenon via nonlinear (prices) to encourage convergence, if needed. Each module of
cost and delay functions, the model can also be executed independently.
· Inclusion of elastic transportation demand,
· Explicit treatment of shippers, The macroeconomic module includes four submodules to
· Explicit treatment of carriers, predict economic activity. Rather than build a model of the
· Sequential solution of shipper and carrier submodels, economy, the Department of Energy rents four models from
· Simultaneous solution of shipper and carrier submodels, Standard and Poor's/DRI: U.S. Quarterly Model of the Econ-
· Sequential solution of macroeconomic model and trans- omy, Personal Computer Model of Industrial Output,
portation network model, Employment Model by Industry, and Regional Model. These
· Simultaneous solution of macroeconomic model and four models each provide some of the variables for input to
transportation network model, the National Energy Modeling System, and provide valuable
· Solution employing nonmonotonic functions, cross check on results and assumptions. The macroeconomic
· Explicit treatment of backhauling, module integrates the results of the four models and provides
· Explicit treatment of blocking strategies, and limited ability to question assumptions within them to pro-
· Inclusion of fleet constraints. vide baseline cases.
"Blocking strategies" refers to means of collecting carloads
into trains for more efficient shipping by rail. The authors State of the Art, Early 1970s
report at length on recent (at that time) attempts to impart
further realism to the models in the areas of simultaneous Smith, P.L., "Forecasting Freight Transport Demand--The
shipper-carrier decision making, including competitive and State of the Art," The Logistics and Transportation Review,
cooperative behaviors, simultaneous macroeconomic and Vol. 10, No. 4, 1974.
network models, and fleet constraints. Issues that have not
been handled well according to the authors are backhauling This is an excellent review essay about the evolution of
and blocking. six major approaches to freight-demand forecasting through
the early 1970s. The author reviews 44 articles relating to:
(1) market share models, (2) I-O models, (3) inventory theo-
Whole Models retic models, (4) gravity models, (5) abstract mode models,
and (6) linear programming models. The paper focuses on
National Energy Model the assumptions behind and limitations of each approach.
The author emphasizes the tradeoff between analytical or
U.S. Department of Energy, Energy Information Administra- theoretical sophistication and the amount of data necessary
tion, Office of Integrated Analysis and Forecasting, The Na- for calibration. The simplest and most prevalent technique
tional Energy Modeling System: An Overview 2000, Mar. 2000 is mode-share models, but gravity models and linear-
[Online]. Available: http://www.eia.doe.gov/oiaf/archive/ programming models offer better policy sensitivity and
aeo00/overview/index.html. should extrapolate better to future situations. The author cau-
tioned against I-O models with advice that is still relevant:
The National Energy Modeling System is used to forecast "The fundamental problem of using input-output models in
U.S. energy production, demand, and prices over 20 years. multiregional or multi-country analysis is the massive data
The model is composed of a series of modules. Each module requirements. This problem would be even more severe for
includes a single type of supply, conversion, demand, or other a modally disaggregated, transport oriented inter-regional
input or output of the system. Modules include macroeco- input-output model."
nomic activity, carbon emissions, transportation demand for
energy, electricity markets, oil and gas supplies, coal markets,
and more. The results of the model are one input to the annual State of the Art, Late 1970s and Early 1980s
U.S. Department of Energy Annual Energy Outlook report.
Winston, C., "The Demand for Freight Transportation:
Each annual iteration of the forecasting model includes Model and Applications," Transportation Research, Vol.
multiple baseline cases and changes in assumptions. For ex- 17A, No. 6, 1983.
ample, the model for 2000 included 5 baseline cases plus 32
nonbaseline cases to explore the impacts of varying key as- In what could be described as a follow-up review essay to
sumptions. This method of varying key assumptions is not the one by Smith (1974), Winston reviews several demand for-
significantly used in freight transportation forecasting. mulations developed by researchers through about 1981. This
article was written during a period of deregulation in the U.S.
The "integrating" module expedites changing assumptions freight industry; therefore, the review was slanted toward
by ensuring data uniformity among modules and by testing those models that would help readers understand deregulation
module output for iterative convergence. The integrating issues. The article offers opinions as to the most worthwhile
module automatically relaxes some impedance parameters directions in freight-demand models, promoting disaggregate
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113
models, particularly those with behavioral underpinnings, over inating, destination, and through flow was adjusted for inter-
aggregate models. The paper emphasizes the need to look at national imports and exports, with the port shown as
a full range of options that relate to the mode selection deci- "through" rather than origin or destination.
sion by shippers, including shipment size, service quality, and
location. The author briefly discusses "inventory" models that The ton-mile estimate error may be up to 7% off, owing
reflect decisions made by the receiving firm. to the disparate data sources.
This study used a simple model and simplifying assump-
Combined Model tions for the network. It did not go through the four-step
model, instead loading tons, origins, and destinations into
Chang, E., A. Ziliaskopoulos, D. Boyce, and S. Waller, "So-
ton-miles on the network. The goal was a set of ton-mile es-
lution Algorithm for Combined Interregional Commodity
timates, not assigned trips.
Flow and Transportation Network Model with Link Capac-
ity Constraints," Transportation Research Record 1771,
Transportation Research Board, National Research Council, Feedback to Generation
Washington, D.C., 2001, pp. 114121.
Park, M. and R. Smith, "Development of a Statewide Truck-
This paper describes a classic regional, statewide, or in- Travel Demand Model with Limited Origin-Destination Sur-
terstate model framework. The initial data are I-O tables and vey Data," Transportation Research Record 1602, Trans-
transportation network, and the final output is OD demand portation Research Board, National Research Council,
for each node in the network, link volumes, and system cost. Washington, D.C., 1997, pp. 1421.
The model has an entropy coefficient to find cross-hauling
and dispersion effects. The authors explore a method of creating statewide OD
tables using very limited initial data and a selected-link-
I-O flows are converted to commodity flows and to truck- based (SELINK) analysis. This method, applied to a
loads using different conversion factors for each commodity. statewide model using OD data from only 14 of 624 zones,
The model goal is a combination of OD demands and link underreported trips by only 18%. The goal is to provide a tool
volumes that result in optimum system cost. Once the OD to lower the cost of data.
demands are set, the algorithm uses DanzigWolfe decom-
position to distribute the flows to the network. SELINK analysis is a feedback process from traffic as-
signment back to trip generation. The entire trip generation-
The test network included 36 zones and 13 commodities. to-gravity model-to-traffic assignment and then feedback to
The algorithm functioned as expected, but was not validated trip generation process requires three iterations to provide
or calibrated. best results. Each selected link is compared with known vol-
umes after traffic assignment, and an adjustment is com-
The model recognizes congestion and capacity constraints, puted. For the statewide model example, there are 32 selected
but not truck weight constraints or time-of-day issues. links.
Details of the methods, algorithms, and data requirements
Use of Secondary Data Sources are clearly shown in the paper. The study covered internal
internal trips only. Error is measured by RMSE. In the
Chin, S., J. Hopson, and H. Hwang, "Estimating State-Level statewide model example, RMSE for Interstate highways is
Truck Activities in America," Journal of Transportation and 24%, for U.S. highways 46%, and for state highways 104%.
Statistics, Jan. 1998, pp. 6374.
This study estimates the amount of freight shipped by State-of-the-Art Review
truck within, to, from, and through each state. The data come
from the 1993 Commodity Flow Survey, the 1992 Census of Pendyala, R., V. Shankar, and R. McCullough, "Freight
Agriculture, the 1992 Truck Inventory and Use Survey, the Travel Demand Modeling: Synthesis of Approaches and De-
1993 and 1994 Transborder Surface Freight data, the 1993 velopment of a Framework," Transportation Research
U.S. Waterway Data, and the 1993 county business patterns. Record 1725, Transportation Research Board, National Re-
search Council, Washington, D.C., 2000, pp. 916.
Truck flows were assigned to the Oak Ridge National
Highway Network. Assignment was based on shortest path, The article offers a very good review of recent and his-
with a travel time impedance factor. torical trends up to 1999, and then develops a conceptual
framework for freight transportation planning. The authors
Agricultural trips were estimated and added. Import and briefly review freight forecasting and data requirements,
export freight was estimated and added. Assignment of orig- adding nothing new.
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114
This is a good introduction to the field or a primer on the modity flows between BEA regions, consisting of both local
subject. It is a good brief summary of work in the field. and interregional traffic. The model's estimates of major
trends and patterns in transportation cost and traffic levels are
reasonable, although local traffic estimates were not accurate.
Sequential Models
The next vision was the Transportation Systems Center
Ashtakala, B. and A.S.N. Murthy, "Sequential Models to De- Freight Energy Model, which allowed modal choice and
termine Intercity Commodity Transportation Demand," Trans- routing decisions to be based on the energy consumption.
portation Research A, Vol. 27A, No. 5, 1993, pp. 373382.
The Transportation Systems Center model extensively re-
vised the network and operation database. The links and
The objective of the study is to determine the demand for
nodes in the network were modified, and the transit time,
commodity transportation using the conventional sequential
energy use, and cost data were reestimated.
modeling approach. The first three stages are commodity pro-
duction and consumption, distribution, and modal split. The
The National Energy Transportation Study transportation
route assignment stage is not included because the conventional
network model expanded the study area and modified the
all-or-nothing assignment is not found to be adequate for pre-
dicting commodity transport volumes on the highway network. network database. Equilibrium-seeking traffic assignment
routines were developed for the study and were used to pre-
Survey data were gathered and log-linear and logit models dict flows on the model network. The effect of the equilib-
were developed for modal split and an optimized gravity model rium assignment is described in the paper.
was developed for distribution. Commodity demands were rep-
resented graphically in the form of commodity-flow diagrams The Electric Power Research Institute (EPRI) model fo-
between origins and destinations. The diagrams are similar to cused on the network effects of energy supply. The railroad
desire line diagrams and show demands for rail and truck. routing algorithm developed by EPRI was much more
detailed than before. Results from the EPRI model are not
From the study it is evident from commodity-flow dia- reported in the paper.
grams that the nearest source supplies the necessary com-
modities to the communities around it and as the distance Across the different visions, the greatest need for a trans-
increases, the amount of interchange between the sources and portation network model is a comprehensive interregional
sinks diminishes. commodity-flow database. Cost is the most important poten-
tial source of error in the modal choice and routing algorithms.
The study shows that sequential modeling can be applied
effectively for estimating commodity flows. The gravity
model is also found to be applicable for various commodity Underdeveloped Regions
categories. The modal split model used in the study is useful
and innovative. Lastly, the source nearest a sink supplies the Jones, P.S. and G.P. Sharp, "Multi-Mode Intercity Freight
necessary commodities to it. Transportation Planning for Underdeveloped Regions,"
TTR, P523 (incomplete reference).
State-of-the-Practice Review, Early 1980s This paper describes a freight model for parts of eight
states between Brunswick on the Georgia coast to Kansas
Bronzini, M.S., "Evolution of a Multimodal Freight Trans- City--a corridor that is approximately 1,200 mi long and 100
portation Network Model," Proceedings Transportation mi wide. The transportation system there includes several
Research Forum, Vol. 21, 1980, pp. 475485. Interstate, secondary rail lines, and waterways. The Standard
Industrial Classification codes are used to describe com-
The paper describes the development of different national modity groups and there are separated arcs in the network for
multimodal freight transportation network models that oc- highway, rail, and water modes. This is a conventional model
curred at different times and with different visions. All are consisting of 111 zones and 53 commodity and industry
associated with the national network project, which encom- groups. For mode split, transport time and cost are indepen-
passed rail, highway, waterways, and pipeline networks dently derived from the network.
developed in late 1960s.
The INSA project developed a model that could determine Modifying a Four-Step Model
the lowest cost path by considering both the shipment cost and
the cost of delay as perceived by shippers. Node and link char- Kim, T.J. and J.J. Hinkle, "Model for Statewide Freight
acteristics, which are related to the time and cost in the net- Transportation Planning," Transportation Research Record
work, are described. The model was applied to a system that 889, Transportation Research Board, National Research
contains waterways and railroads. The model used the com- Council, Washington, D.C., 1982, pp. 1519.
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115
The authors developed a multicommodity, multimodal port Policy--An Idea Study," Nov. 2001 [Online]. Available:
statewide freight transportation planning model by modify- http://www.sika-institute.se/utgivning/sam01_1.pdf.
ing the existing Urban Transportation Planning System
(UTPS) package developed by FHWA and the Urban Mass SIKA is the Swedish transportation statistics bureau. This
Transportation Administration. There are five classes of study for a model framework draws mostly from the QRFM.
submodels: network analysis, freight transport demand The paper is an exploration of how to make the QRFM
analysis, vehicle requirements, assignment, and evaluation. framework work with existing Swedish statistical reports and
Freight transport demand analysis was done in four steps: software.
freight volume generation, interzonal commodity distri-
bution, modal split, and freight volume assignment. The model begins with economic assumptions from the
UTPS.ULOGIT and UTPS.AGM were used in the calibra- Ministry of Finance, analogous to the U.S. Department of
tion of modal split and commodity distribution from freight Commerce, data on employment, and manufacturing value.
volume OD data. Truck backhaul was estimated from the Matrix estimation uses employment disaggregated to zonal
volume to be carried, the distance, truck size, cost, and OD level. Similarly, through the four-step process, the authors
table. A separate program dealt with empty rail car move- explore local parallel data sources and software to stay close
ments. UTPS.UROAD was used to assign trucks and cars to to the QRFM method.
different networks.
Other
An Early Application in Florida
Morlok, E. and S. Riddle, "Estimating the Capacity of
Middendorf, D.P., M. Jelavich, and R.H. Ellis, "Develop- Freight Transportation Systems," Transportation Research
ment and Application of Statewide, Multimodal Freight Record 1653, Transportation Research Board, National
Forecasting Procedures for Florida," Transportation Research Council, Washington, D.C., 1999, pp. 18.
Research Record 889, Transportation Research Board,
National Research Council, Washington, D.C., 1982, pp.
The authors present a method of measuring the capacity
714.
of an entire system, rather than individual links or compo-
nents. Given a network with known capacities of individual
This paper documents an early effort to create a statewide
components, plus known traffic patterns (OD pattern), plus
freight forecasting model for Florida. The general method
fleet size, the 13 equations of the algorithm will estimate the
was OD table factoring and assignment.
system capacity. The system capacity can be compared with
the existing flows. Additionally, a modified method can be
used to estimate capacity change resulting from change in the
Belgium network or fleet size.
Van Herbruggen, B., In-Depth Description of the Tremove
Model, Transport & Mobility Leuven; Leuven, Belgium, The authors used a very small rail network for their ex-
Mar. 2002 [Online]. Available: http://www.tmleuven.be/ ample. Applications or potential to forecasting or modeling
Expertise/Download/Tremove_Description.pdf. are not discussed. The 13 equations are shown.
The TREMOVE model is a Belgian model to forecast Don Breazeale and Associates, Inc., "Task II--Data Col-
emissions. It is used to model changes in policy and tech- lection Strategic Analysis Report for Strategic Planning
nology on air pollution, and is not suited for forecasting Advice for Freight/Truck Model Development Project, Pre-
freight. pared for Los Angeles County Metropolitan Transportation
Authority, Oct. 2002.
Freight demand is based on mode, price, and time of day.
Freight supply is based on price of vehicle and price of fuel. At 234 pages plus Executive Summary, the report covers
There is no network, no distinction between freight types, only Task II (data collection strategies) of Los Angeles
and no infrastructure. Interestingly, there is time-of-day sen- County MTA's regional Freight Forecasting project. It does
sitivity and multiple modes. not include the model. The report has a useful summary of
data sources, methods, and technologies, some of which are
useful for statewide forecasting. No new methods are devel-
Sweden oped. The consultant recommends long-term relationships
with major shippers as a source of reliable OD data. Also in-
Swedish Institute for Transport and Communication Analysis cludes an annotated bibliography of data sources for regional
(SIKA), "A Conceptual Framework for Analysis and Model and statewide modeling. Very complete and usable as a ref-
Support for Swedish Studies of Freight Transport and Trans- erence guide.
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A Typology it is more important to focus on the economic sectors for the
freight traffic demand because most state and regional eco-
Souleyrette, R., T.H. Maze, T. Strauss, D. Preissig, and A.G. nomics are dominated by a few sectors. The freight planning
Smadi, "Freight Planning Typology," Transportation Re- "typology" focuses on addressing the needs of state and re-
search Record 1613, Transportation Research Board, National gional transportation planning. The first step is to identify
Research Council, Washington, D.C., 1998, pp. 1219. key issues. Freight is divided into groups with the same trans-
portation requirements. Each commodity or sector becomes
Most models built for freight transportation are based on a layer. Sectors are overlayed to form an aggregate forecast
two concepts: spatial price equilibrium and network equilib- of all freight traffic volumes. The paper used a case study of
rium. Most of these models have had implementation diffi- "meat product and farm machinery industries in Iowa" to
culties that have limited their use. The authors contend that demonstrate the method.
