Exhibit F.2. Estimating effectrs of inflation.

-

Contribution to

1988 1gg4 Growth Ratio Cost Change

cosr component Data Sourc€ v;he valu€ 1988-1995r w€ishtr (5) x (6) -

Drivers U.Éi. Bureau of Labor Statistics (BLS),

ffiH,ååjiË:^T,{ilåiä.1',åË*tp", $1224 $13 48r 1145 288' os2t

Vehicle BL|S Producer Price Index (PPI) for

L""tr ov"i i0,000 lbs. CVW (Series 712.4 738.7 1,.278 'l,9Vo 0.243

1106).

Fuel U.fi. Department of Energy, p4e-rgy

lnfrrrmãtion Administation, U.S.

average retail di*"iÌiåìïi;;"p". $1'253 $1'1264 0'901 20vo 0'180

galloni

Tires BL|S PPI for tires (Series 1201). 93.6 97 .8 1.053 3% 0.032

Repair :låiätäiåtliff"1iå"X"ll"o"* 1,'rs.7 1s0.2 1.303 evo o.'I'rT

(sEt 49).

Overhead U.fì. Bureau of Economic Analysis,

Imolicit GDP deflator (current dollar ëifËäfu"däüiåì,äàääãiiåiäöb. 1.03e 7.261, 1'254 21vo 0'263

M

Overall Adjustment Factors 1.156

I See text.

'? 1993 value. t Model's forecast of 1995 fuel pricer in 1988 dollars' 4 Average fuel price, week of Mày 8,1995'

N)
t22

index (PCU4213) was initiated by the Bureau of Labor Statistics in June 1992.Its subcomponents include indexes corresponding to agricultural trucking (#I),LTL general freight

(#3lI), and truckload general freight (#312).

Software Packages

This subsection discusses software packages for performing detailed analyses of truck costs. Packages from two vendors are discussed below.2 The first package, MicroTOCS

(produced by Snavely, King & Associates of Washington,

D.C.) is primarily designed to provide detailed cost estimates

for individual truckload (and multi-truckload) shipments.

The second package, the Truckload Cost Information System

(TLICIS) (produced by the Transportation Consulting Group

of Bethesda, Maryland), has a more carrier-oriented focus,

producing less cost detail for individual shipments but a substantial amount of aggregate information describing the

overall costs incurred by truckload carriers. Both vendors

also have corresponding packages for analyzing costs for

LTL shipments and operations.

The prices of the software packages vary with: the specific

configuration (PC versus mainframe version); amount of

technical support needed; whether any consulting services

are required in preparing data for input into the models and

in interpreting results; and the number of work stations and

sites involved. The Transportation Consulting Group charges

$3,900 for a site license for TLICIS and $250 per update.

M|UoTOCS

MicroTOCS is a package produced by Snavely, King &

Associates for estimating the costs of truckload movements.

In MicroTOCS, the basic costing unit is a truckload shipment

from a particular origin to a particular destination. The shipment costs can be reported in a variety of ways, including:

total operating cost per mile; total cost per mile; total cost per

ton; and total cost per ton-mile. Total cost includes equipment capital cost, while operating cost does not. All costs

exclude loading and unloading costs and the cost of any associated waiting time. However, empty return ratios are

reflected in the cost estimates.

Linehaul operating costs are estimated as consisting of:

driver costs; fuel costs; miscellaneous costs; tires; maintenance; user taxes; and administrative and overhead. The estimates of tire and maintenance costs distinguish the tractor

and trailer components; and insurance costs and licensing

and permit charges are also distinguished. Total costs include

all operating costs plus capital costs for tractors and trailers.

The operation of the system requires the user to move

through a series of menus specifying model inputs. The first

menu requires specification of shipment characteristics: total

tons; tons per trailerload; trip distance; and estimated empty

return mileage. The remaining menus provide default settings for various costs required by the model and provide the

user with the opportunity to modify these settings.

Truckload Cost Information System

The Transportation Consulting Group's Truckload Cost

Information System (TLICIS) is designed primarily for use

by truckload carriers for analyzing the costs of various

aspects of their operations. Accordingly, TLICIS provides

less detail than MicroTOCS about costs relating to individual shipments, though, unlike MicroTOCS, it does reflect

data on pickup, delivery, and unloading costs. With its

broader focus, TL|CIS makes greater demands on the user

in terms of data gathering and information collection and

provides correspondingly more detail.

The underlying basis for TL/CIS is the activity-based

accounting system required by the Interstate Commerce

Commission in its annual report process. This accounting

system requires Class I and II carriers to break down all cost

categories by activity. For example, driver wages are broken

down into wages paid for pickup and delivery activities versus those paid in linehaul activities. Accordingly, the TLICIS

model requires carriers to break down all of their costs into

individual components and then account for the cost components by activity. The major activity categories for a truckload carrier are linehaul, loading, unloading, stopoff, claims,

and billing and collecting.

At the most disaggregate level, the progam produces output in terms of costs for a particular shipment (with origin and

destination specified) or a round-trip movement. In contrast

to MicroTOCS, however, TL/CIS reports not only the total

linehaul costs for a shipment, but also includes costs for

pickup, delivery, unloading and any stopoff costs. In TLICIS,

the basic unit of costing analysis is costs per mile. There is no

information on type of commodity, nor is there any effort to

determine load capacity, cargo weight, cost per ton, or cost

per ton-mile. Instead, the program is designed to operate in

batch processor mode to summarize and report on shipments

from a particular terminal, on a specific traffic lane, from a

particular customer, or by a particular salesperson, during any

given time period. These summary reports form the backbone

of the costing system. It allows the carrier to determine how

specific terminals, traffic lanes, accounts, and salespeople are

doing and where trouble spots exist within the entire system.

TL/CIS has a module designed to provide the interactive

costing of specific loads and trips by means of an on-screen

"input 1og" which the user completes to describe a particular

move. This input log gives the user the flexibility to select

from a choice of over 100 different driver and equipment

configurations.

RAILROAD RATES AND COSTS

Average 1992 ralkoad rates per ton-mile a¡e summarized

in Exhibit F.3 for selected major commodity groups. The

2 Some olher firms that have developed proprietary models for estimating truck costs

are IBI Group (Toronto), Peat Marwick Stephenson & Kellogg (Toronto), and

Trimac Consulting Seruices (Calgary, Alberta).
Exhibit F.3. Average raÍl rates per ton-mile for selected commodity

groups.

STCC Code a¡rd Commodítv Group

01 Farm Products

11 Coal

74 NonmetallicMinerals

20 Food Products

24 LumberandWoodProducts

26 Pulp and Paper Products

28 Chemical Products

29 Petroleumand Coal Products

32 CIay, Concrete, Glass, and Stone Products

33 Primary Metal Products

37 TransportationEq¡ripment

40 Waste and Scrap Materials

42 Empty Shipping Containers

46 Miscellaneous Mixed Freieht

commodity groups shown in the exhibit account for about 87

percent ofrail tonnage and 88 percent ofrail revenue.

The average railroad rate in 1992 was 3.03 cents per tonmile. The average rate in 1994 can be estimated by multiplying by 1.017 (from the U.S. Bureau of Labor Statistics,

PPI for railroad line haul operations) to produce a rate of 3.08

cents per ton-mile. Extrapolating an additional year produces

an adjustment factor of 1.026 and an average rate of 3.11

cents per ton-mile for 1995.

Rates per ton-mile tend to vary inversely with length of

haul, size of shipment, and commodity density. If rate and

cost estimates are required that reflect the effects of these

influences on actual transport costs, estimates can be obtained

using the ICC's Uniform Rail Costing System (URCS).

Computer implementations of this system are available from

several commercial sources. One such implementation,

MicroURCS (produced by Snavely, King & Associates of

Washington, D.C.) is discussed in the subsection below.

For many purposes, less precise rate estimates should

suffice and data presented in Exhibit F.3 should prove adequate. The average rates for the commodities shown in this

exhibit are all between two and four cents per ton-mile with

one very significant exception: the average rate for transportation equipment is 9.01 cents per ton-mile. This high rate

occurs primarily because of the low density of assembled

motor vehicles (which average only 22 tons per carload as

compared to an average of 66 tons per carload for all

commodities). The lowest average rates are for coal and

farm products (particularly grain), both of which are frequently shipped by unit train, qualifying for significant

volume discounts.

All Commoditíes

Source: Interstate C-omrrrcrte C-ommissiory 7992 Caiload Waybill SamVle,

CD.ROM.

Cents per Ton-Mile

(1992 Dolla¡s)

2.79r

2.10

2.98

2.92

2.89

3.93

3.90

4.03

3.59

3.18

9.01

3.83

3.83

2-97

123

3.03c

A Software Package

This section discusses a software implementation of the

ICC's URCS. The package, MicroURCS, was developed by

Snavely, King & Associates (of Washington, D.C.). Its price

varies with the specific configuration (PC versus mainframe

version); ¿rmount of railroad-specific cost data purchased;

amount of technical support needed; whether any consulting

services are required to prepare data for input into the model

or to inte¡pret results; and the number of work stations and

sites involved.

For each shipment, the minimum information required of

the user by MicroURCS consists of commodity, tonnage

shipped, railroads used, and mileage on each railroad. With just

this infnrmation fhe nrôqrâm will sef a numher of defarrlt val- -"t -"- r- - o-'

ues to estimate railroad costs for the shipment. Altematively,

users can provide additional data, overriding the defaults, and

enabling the program to produce better cost estimates.

The ICC's URCS is a three-phased process. Phase I involves an exhaustive regression analysis based on data supplied by the railroads. Phase 2 involves the conversion of the

regression results into unit costs and operating parameters.

The third phase uses unit costs and operating parameters,

along with the detailed shipment characteristics supplied by

the user, to determine shipment costs.

Within the structure of MicroURCS, users can change

pa-rameters and assess the impact of these changes on shipment

costs. The sensitivity analysis, making individual changes

and assessing impacts, is straightforward and a strong point of

this software package.

MicroURCS develops cost estimates for specific, individual railroad shipments from data supplied by the individual
124

railroads and analyzed through the URCS. If the user of

MicroURCS purchases costing data from all the individual

railroads, then cost estimates for a given movement will be

based on data from the actual railroads participating in the

movement. Otherwise, the cost estimates will be derived from

data from all the railroads operating in the appropriate region.

The user begins the input process by specifying information about the commodity being moved and its specific

requirements. The user specif,es the commodity; whether a

TOFC/COFC movement is involved; the net tons in the shipment; whether any protective services, accessorial services,

or refrigeration services are needed; and whether the shipment

involves movement of motor vehicles.

Users a¡e then directed to provide information on the equipment used, routing, and handling requirements for the shipment. If the shipment is a TOFC/COFC shipment, the user is

asked whether the railroad or the shipper is supplying the

trailer/container and the number of units involved. The program requests information on the number of cars, car type

(with default car types selected on the basis of commodity

specification), and whether the cars are supplied by the railroad or by the shipper. The program also requests information on the specific railroads in the route, the mileage on each

railroad, the origin and destination, and any special handling

required at the origin or destination. As previously observed,

default values can be used for all inputs except commodity,

tons, and mileage by railroad.

The program next moves into a costing section based on

the line-haul cha¡acteristics of the shipment. This involves

determination of whether the specific shipment is part of a

multi-car shipment. Switching costs at origin and destination

are reduced by 75 percent for unit-train movements and

by 50 gram computes car-miles, gross tons, gross ton-miles,

number of intra-/inter-train switches, and interchange

switches. These inputs and intermediate results are used

to develop detailed cost data for the individual shipment. The

program calculates total variable costs per car, per net

ton-mile, and per net ton, and also fully allocated cost per

net ton-mile.

WATER TRANSPORTATION

Types of Operation

The structure for the cost analysis depends on the operating

and market pattems specific to va¡ious vessel and service types.

The various types of water transportation can be categorized:

. inland barge;

. intercoastal tug-barge;

. deep-sea bulk carriers (liquid, dry, and mixed);

. deep-sea breakbulk carriers (liner and tramp); and

. deep-sea container carriers (liner).

Inland barge and intercoastal tug-barge operations generally are limited to the carriage of bulk commodities. Barge

transport includes the dedicated transport of single commodities between two points, as well as common carrier distribution of common barge types between river systems. As

with other bulk operations, the shipper often owns and operates dedicated equipment, particularly for coastal operations.

Inland barges typically are moved in multi-barge tows which

may combine barges for several shippers. For example, an

upper Mississippi tow operator may pick up grain, fertilizer

and chemical barges at va¡ious points on the upper river for

transfer to operators on the lower Mississippi at an interchange point. On the other hand, dedicated services will

shuttle between a limited number of river points, with the

towboat often waiting with the barge for the return.

Deep-sea operations are primarily distinguished by the

combination of vessel type and operating pattern. "Tramp"

operations are based on single voyages moving one or more

commodities, often on a single charter basis. Bulk and highvolume or seasonal breakbulk commodities typically move

in this fashion. Tramp operations generally include full

shipload lots and empty deadhaul legs between discharge and

load ports. Some vessels may be dedicated to a particular

cargo flow (e.g., Alaskan oil carriers), or may shift between

trade routes and commodities on a seasonal or market-driven

basis (e.g., tramp refrigerated vessels).

"Liner" operations are based on multi-commodity markets

using multiple vessels in fixed port rotations and schedules.

These services are designed for containerized and general

breakbulk cargoes, and typically make multiple port calls

over a coastal range. For example, a North Atlantic carrier

might operate four vessels with weekly calls at Charleston,

Baltimore and New York in the U.S. and Felixstowe and

Rotterdam in Europe. A single voyage for each vessel would

take 28 days. Container operators also maintain inland distribution systems for their containers, which must also be

considered in a costing analysis.

Cost Elements

Cost and time analysis for water transport facilities can

either be based on the marginal impact on existing rate and

service characteristics or the development of fully-allocated

system costs and service parameters for representative cargo

flows. The virtual elimination of meaningful tariff rates for

most water transportation limits the ability to use rate analysis for public use facilities with a mixture of commodities.

The more conìmon method is to estimate the underlying cost

and transit-time structure, calibrated against available rate

and service data. This section examines the specific elements

used in water transport costing, provides some representative

sources, and also identifies critical issues which affect the

results.

Total transport costs can be estimated from a combination

of physical characteristics, operating and productivity factors, and unit cost elements. The physical characteristics

relate to items such as vessel type, cargo handling equipment,

and commodity density. Operating and productivity factors
include vessel and cargo processing time, fuel efficiency, and

vessel speed. The unit costs are typically based on volume or

time and are combined with operating estimates to generate

total system costs.

Inputs for water transportation costing can be categorized:

. vessel;

. voyage and port;

. cargo-related; and

. inland and other.

Vessel-related inputs encompass physical and cost characteristics which apply regardless of the voyage or service

patterns (with some exceptions). The physical characteristics

of the vessel affecting costs include:

. type and utilization (e.g., tramp bulk vessel);

. physical dimensions (for accessibility and port charges);

. capacity (cargo load and design speed);

. operating efficiency (fuel consumption and maneuverability);

. manning requirements;

. safety characteristics (for annual repair and insurance

estimates); and

. annual operating availability (for allocating annual costs).

The cost inputs associated with the vessel include:

. capital or lease cost (on annual or other basis);

. annual insurance (hull and machinery, personnel and

injury);

. maintenance and repair (periodic and overhaul);

' supplies and stores;

. crew costs;

. fuel; and

. administrative/overhead.

125

is defined by the specific port calls, the voyage length, and

the time or distance under low-speed operations (e.g., canal

transit). Vessel itineraries frequently change, sometimes

requiring a definition of a prototypical voyage for analysis.

A tramp operation will typically assume a direct route

between the load and discharge ports at full operating speed

unless a lower speed is appropriate based on fuel economy.

In many cases, tramp operations will include a deadhaul

(e.g., empty) leg from the discharge port to the next voyage's

load port, some portion of which may be allocated to the

previous voyage.

Costing for liner operations is typically based on the entire

itinerary rather than specific port pairs. A liner operation

following a fixed schedule may operate at a lower-thanmaximum speed which can be calculated based on available

sea time (i.e., round-trip voyage time minus port and other

delays).

Transit time for inland barge operations may include

delays for lockprocessing which often represents a large portion of total transit time. Lock delays can be measured from

historical data or approximated using models which combine

lock-processing efficiency with traffic-fl ow and tow-arrival

patterns.

Port characteristics may be estimated for each port or

within general categories (e.g., domestic and foreign). Operating factors include berthing delays (in fixed hours or days

per voyage), vessel berthing time (based on vessel size and

berth type), and cargo handling time (based on load characteristics, equipment type and stevedoring practices). The

term "port delay " is typically used to denote an unusual circumstance (e.g., berth congestion) which extends the port

time, but may be applied to the entire port time in some cases.

Costs which are specific to a voyage itinerary include vessel tolls and port-related charges applied to the vessel (often

based on size) and the cargo transferred (mostly based on

type and volume). Vessel-related port costs include dockage

and pilotage, while cargo-related costs include wharfage and

cargo handling costs. Some port costs are fees assessed by

the port as reimbursement for use of public facilities. Most

cerøo handline côsts in deen-sea trades are charsed bv ori- ---Þ- ""^------o - ---- --.' -, - -f

vate stevedores based on the required manpower and equipment to load and discharge the vessel. Typical charges

include stevedoring, terminal handling, equipment rental,

and container stuffing and stripping. Practices for port

charges may vary by U.S. coast and for foreign countries.

Bulk and domestic barge operations typically utilize private

terminals, often with no public port involved. Costs for these

operations must be estimated from private sources.

Sources for voyage itineraries include Lloyd's on-line

databases, the Journal of Commerce Shipcards and other service listings. Port operating characteristics and non-public

cargo handling costs must typically be developed through

private interviews; although, in most cases, costs and operating factors will be common by general cargo type over a particular port range. Fees charged by ports usually are available

from port tariffs, although ta¡iff rates may not apply to highvolume users. Worldwide port directories (e.g., Lloyd's

Annual costs for capital/lease, maintenance and insurance

are generally specific to a particular type of vessel, but may

"--' '.'ifl^ fLô rr¡ña ^€ "tili-.ti^- ¡'- n hifth riaL r¡n¡¡ococ\

Y oJ vv¡r¡r \e.ó.r r¡¡6¡¡ r¡o¡\ r vJ 46vul.

Supplies and stores and crew costs can be estimated on an

annual or daily basis. Unit fuel costs vary with fuel type and

the point of purchase. Administrative and overhead costs typically are estimated as a percentage of all other costs (perhaps

excepting capital costs).

Sources for vessel data include: Lloyd's Registry of Shipping (and an associated on-line database); the U.S. Maritime

Administration; special industry reports by Drewry Shipping

Consultants of London; and various industry journals (such

as C ont ain e ri zatio n Int e rnati o nal, Lloy d' s S hip p in g E c o no -

mist, Marine Log, Maritime Reporter and Engineering News,

and Waterways Journaï¡. As with other costs, costing for

international operations considers the effect of currency

exchange rates when appropriate.

Voyage and port inputs are specific to a particular use of a

vessel, varying by trade route and commodity market. The

vessel itinerary dictates many of the cost and time factors and
126

Ports of the World) often include summary information on

port tariffs, facilities and rules.

Cargo-specific inputs affecting cost and time factors

include physical, market and packaging characteristics. The

physical stowage and load cha¡acteristics determine the utilization of vessel and container capacity, while the handling

and storage requirements dictate the type and cost of port

facilities and equipment. The market characteristics include

the balance of flow which determines port handling time and

costs, as well as load factors for liner operations and required

vessel size for tramp operations (including backhaul opportunities). When cargo is palletized or containerized, the cargo

weight/unit relationship affects cargo handling factors.

The cost of container and pallet systems may also be

included in the cost analysis. Pallet costs are based on an

allocated share ofthe unit cost for reusable units and the total

costs for one-way pallets. Container system costs can be estimated by calculating capital and operating costs for a fixed

ratio ofcontainers to available vessel capacity ("slots"). For

example, a service of four vessels with a capacity of 800

twenty-foot equivalent container units (TEUs) might require

three containers for every TEU ofcapacity or a total of7,20O

TEUs. The "additional" containers are either resting in a port

or involved in the inland transit.

A final cargo-related cost which may be considered is the

inventory cost which measures the financial cost of "holding"

the commodity during the transit period. While this cost is not

included in the ftansport rate or typically calculated at all, it provides a basis for measwing the irnpact of delays and changes in

transit time, including changes resulting from changes in mode

and./or route. Inventory costs can be calculated based on the

average value per weight or container unit, combined with

interest rate which reflects the cost of the capital required to

hold the commodity for the period of transport.

Sources for cargo-related physical inputs include Thomas'

Stowage (which provides weight-to-volume ratios on a commodity basis) and cargo flow statistics which cover both

weight and volume data (e.g., PIERS and Census sources

described in Appendix C). Data for major commodity flows

may also be available through Drewry and other industry

sources. Flow patterns can be measured from traffic statistics (available from PIERS, Census and individual ports).

Other data must be generated from interviews or general

commodity or water transpofation sources.

The final category ofcosts is for inland transpof which can

be estimated using the appropriate modal costing methodology or assumed at a fixed unit cost if not influenced by the

new facility. A key consideration for many general cargoes is

the split between rail and truck flows. This information is not

generally available but may be collected through a survey or

interview process or approximated from a disaggregation by

geographical region (i.e., rail vs. truck hinterlands).

Cost-Estimating Methods

Total and unit transport costs can be estimated by generating total capital and operating costs for an annual operation

or single voyage, and then appropriately allocating the costs

over the traffic flow. The first step is typically to calculate the

total voyage time for use in generating time-based costs.

Other cost factors are combined with the appropriate "use"

factor (e.g., crew man-days or tons loaded and discharged).

The allocation ofannual costs on a voyage-basis requires the

following steps:

. calculate total annual costs;

. estimate projected annual operating days (excluding

maintenance and other downtime);

. calculate daily cost by dividing annual costs by operating days; and

. calculate voyage costs by multiplying daily costs by

voyage days.

Fully allocated unit costs for bulk and breakbulk commodities are usually stated on a "per ton" basis, while containerized cargoes may be either per ton or per container or

TEU. Costs may also be stated on a ton-mile basis, particularly if port and voyage costs are insignificant or measured

separately. The unit costs can be calibrated against rate data

if available.

Some key issues which often apply to water transport

costing include:

. Capital Costs: Accounting or Economic---The estimation ofcapital costs on an accounting basis (i.e., depreciation,

principal and interest) may skew results when comparing services with different fleet compositions. The true "economic"

costs can be estimated based on an arnortization of the current sale value over the expected lifetime minus scrap value.

. Allocation of Fixed Cosrs-The volatility of water

transportation markets often creates a disparity between rates

and fully allocated costs due to the method for allocating

fixed costs. It is often useful to segregate marginal and fixed

costs in the analysis, and also to consider current industry

conditions.

. Definition of Cargo Capacity-While most capacities

are stated in weight terms, volume-based restrictions apply

for many breakbulk and containerized commodities. It is critical that vessel loading reasonably reflect the cargo mix,

particularly when comparing different vessel types.

. Allocation to Backhaul Flows-Many waterborne services are designed for one-way movements of specific commodities (e.g., vehicles or bananas) or may have a natural

imbalance in one direction. The backhaul leg is often considered secondary to the main cargo flow and is often sold on

a marginal cost basis. In such cases, an equal allocation of

fixed costs among all traffic understates the costs in the headhaul direction. (The service would probably exist with no

backhaul traffic, in which case, the headhaul traffic would be

assigned all fixed costs). Various adjustments include assigning only the marginal costs of cargo handling to the backhaul

or calibrating the assignment of ûxed costs based on the

relative market rates in each direction.
Exhibit F.4. Deep-draft vessel costs (1995 dollars).

Capacity

(DWTTons)

20,000

s0,000

90,000

150000

265.000

Speed

(Knofs)

20,000

50,000

90,000

150,000

265.000

74

14

74

'1,4

74

Dollars

oer l{or¡r

U.S. Flas

Tanker - Non-Double Hull

$7,592

7,953

2,270

2,625

3.728

15,000

40000

80,000

120,000

200.000

14

14

74

1,4

74

Cmts per

Ton-Milet

Tanke¡ - Double Hull

1.184C

0.581

0.375

0.2ffi

0.176

s7,452

1,981

2,519

3,185

4.228

11,000

20,000

30000

74

't4

1,4

74

74

Dolla¡s

ner flour

Foreien Flae

Capacity

(TEUs)

1.080a

0.5E9

0.477

0.316

0.237

$1,093

'1,,430

1,820

2,136

NA

$639

815

975

1,162

1.440

600

7,200

2,000

2,800

4,000

,17

,17

't7

DrvBulk

Cenb per

Tmr-Milel

127

Speed

(Knots)

I Excludes port fees and tolls.

Source: Study team estimates and data from U.S. Army

Water Resourcæ, FY 1.995 Planning Guiilance: Dcq

Virginia, December 1994.

0.4754

0.243

0.161

0.115

0.081

1.084c

0.532

0.339

0.265

NA

TypicalCosts

Deep-Draft Vessels

$1,059

1,393

'1,721

$s83

826

1,075

1,386

1.880

Gmeral Ca¡eo

77

77

18

19

20

Exhibit F.4 shows some typical costs for deep-draft vessels

by type and vessel size. The costs per hour are a weighted average of fiscal year 1995 Corps of Engineers estimates for vessel costs at sea and in port and exclude port fees and tolls.3 (The

only difference between at-sea and in-port costs is fuel consumption, which represent less than ten percent of total costs.)

For tankers and dry-bulk vessels, costs per ton-mile were

estimated by assuming an average load factor (ratio of cargo

weight to deadweight tons) of 60 percent and that 20 percent

oftime is spent in port. For general cargo vessels, the correDollars

¡er lfcur

0.434(

0.246

0;t78

0.138

0.106

Container

r.259î,

0.910

0.750

$393

561

759

820

l.2M

$909

7,154

'r,517

't,9u

2,293

Cents per

TEU-Mile

0.390c

0.2ú

0.141

0.114

0.090

14.85c

9.43

7.02

6.22

4.78

9472 ilz

67

Dolla¡s

nerlfour

r U.S. Army Corps of Engineers, Institute of \Vater Resources, FY 1995 Ptanníng

Guidance: Deep Drafi VesseL Costs, Fort Belvoir, Virginia. December 1994.

0.490c

0.354

0.297

$544

76

1,101

1,527

1,811

Corps of Engineers, Institute of

Draft Vesscl Cosis, Fort Belvorr,

Cents per

TEU-Mile

sponding assumptions were an average load factor of 15 percent and 40 percent oftime in port. For containerships, costs

per 2O-foot equivalent container unit (TEU) mile were estimated assuming an 80 percent load factor (relative to TEU

capacity) and 20 percent of time in port.

The cost estimates in Exhibit F.4 show very large cost

advantages for foreign-flag vessels relative to U.S.-flag vessels and substantial economies of scale for the larger vessels

(though large vessels, of course, are limited to routes generating high traffic volumes and serving harbors with adequate

channel depth).

Inland Barges

Exhibit F.5 shows some typical costs for operating inland

barges. The costs per hour for barges and towboats are fiscal

E.88a

6.28

5.10

4.78

3.n
Exhibit F.5. Inland barge costs for waterways without locks.

Ba¡pe Tvoe

Deck Barge

Open Hopper

Covered Hopper

C-ommoditv

Geneal C-argo

Gmeral Bulk

General Bulk

Tank -Double

Hull-with Coils

SingleSkin Tank

Chemical Tank

(II) -Coils and

Lined

Dimeneions

(LxWxD)

(Feet)

13ù35x10

195x35x12

195x35x12

195x35x12

Câpacity

(Tons)

Peholeum

Products

Liqr¡id Bulk

Liquid Bulk

Self-Unloader

Tank - Double

Ht¡ll-with Coils

ftessu¡e Tank

Ilessu¡e Tank

Pressu¡e Tank

Operating Cost Operating Coet Barges Load Cents per

PerDay Horsepower perDay perTow Fa@ 19!144{

750

1,500

r,500

1,500

195x35x72

795x35x12

195x35x12

$58.û7

$89.6ó

s79.35

$91.52

Cement

Petsoleum

Products

Ammonia

LPG

Chlorine

I Excludes port fees and the effects of lock delays and transit times.

Source: Study team estimates and data from U.S. Army Corps of Engineers, Institute of Water Resources, FY 7995 Estìmated

Towboat qnd Barge Costs, Draft., Fort Belvoir, Virginia, no date'

1A25

1,425

1,425

12n 52,717 1 62.5vo 3.$É

1,5m 3,109 4 62.5 0.93

7,2m 52,717 | 62.5% 1.56

1,5m 3;tú 4 0.5 0.¡18

lÆm $,109 4 62.5% 0.48

5,6m 7,6n 15 62.5 0.38

1,5m $,109 4 62.5% 0.48

5,6m 7,677 15 62.5 034

Tow Cha¡acteristice

795x35x72

290x50x12

290x50x12

/78xfrx72

ZltxtÁx72

195x35x12

ç256.70

$210.86

fi323.90

1,410

3,300

3,000

2,5û

1,500

1,000

ß37.û

n0.20

$524.51

sl,736.99

51,129.96

s745.61

$3,109

7,677

$3,109

7,677

$3,109

7,677

N)

00

4 557o

15 55

4 557o

15 55

4 55%

15 55

il,w7

6,752

!ß,7s2

%,752

9,t77

M,0n

0.69r

0.51

2 50Eo 2n

2 50?o

2 5O7o

2 50Vo

2 SOV,

0.6

0.48

0.73

0.5ó

2.07

2.24

2.90
year 1995 Corps of Engineers estimates.a The costs per tonmile are derived assuming an overall average operating

speed of 10 knots (upstream and downstream) and tows

underway 80 percent of the time. This last assumption is

appropriate only for waterways without locks. For waterways with locks, an additional (waterway dependent) adjustment is necessary to reflect lock transit and delay times

(decreasing the percentage of time underway and increasing

costs per mile and per ton-mile). These delays can result in a

substantial increase in costs per mile and per ton-mile.

Exhibit F.5 indicates that costs per ton-mile vary significantly with tow size and, to a more moderate extent, with

barge type and commodity (which affects barge capacity)

and with load factor. The load factors shown in the exhibit

reflect assumed backhaul loads of25 percent for dry barges,

10 percent for standard liquid barges, and zero percent for

specialized barges. Four-barge tows are operated on several

waterways, and l5-barge tows are operated on the Illinois

and Ohio Rivers and on the upper Mississippi (and 3O-barge

tows are used on the lower Mississippi). Specialized barges

usually are operated in dedicated service using small tow

sizes.

For most commodities and tow sizes, the barge costs per

ton-mile estimated in Exhibit F.5 are appreciably lower than

the corresponding rail rates shown in Exhibit F.3-in some

cases by a factor of ten. However, as observed above, for most

waterways, the barge costs require a further adjustment to

reflect the effects of lock delay and transit times. Furthermore, if comparisons are to be made with rail rates, an additional adjustment is required to reflect the greater circuity of

waterways. (Barge circuity is estimated to average about 17

percent more than rail circuity.s)

AIR TRANSPORTATION

t29

analysis.) Air freight costs can be estimated based on the

following categories of inputs:

. flight and airport operating characteristics;

. operating expenses (fuel and other);

. airport/station costs; and

. administrative costs.

Similar to water transport, air freight operations can be

separated between scheduled round-trip services ("liner")

and one-way charter flights ("tramp") often requiring an

empty backhaul leg. In either case, the round trip distance

and average operating speed for a particular aircraft can be

used to calculate the round trip "block" hours which designate the period from when the blocks are removed prior to

takeoff and when they are replaced after landing. (Flight distances between ailports are available from a variety of

sources including the U.S. DOT.)

Costing methodologies for air passenger transport utilize

detailed periodic costs for specific carriers, aircraft types and

operating divisions as designated in the Department of

Transportation Form 41 filings and reports. For example,

Carrier A may file a report for all DC-8 aircraft for its Pacific

Division. Unit costs per block hour can be estimated for personnel, equipment, insurance, taxes and other non-fuel operating expenses. Aircraft-related unit costs include maintenance and capital cost (depreciation. amortization and leases)

also state per block hour. Fuel costs could also use Form 41

consumption rates per block hour, combined with the appropriate unit fuel cost for the airports involved. Total flight

operating costs per round trip would combine the unit costs

with estimated block hours.

Ground or "station" costs can be calculated on a trip basis,

as most carriers use contract operators at non-hub airports

(due to the limited number of daily flights). Ground costs

include landing fees, aircraft and cargo handling, crew overnight costs, and miscellaneous airport charges. Landing fees

are usually published rates available directly from the airports, although reductions and exemptions may apply. Air-

^-õît õñ¡l ^..-^ L.-,-ll;-ô ^^oro ¡-^--,'l ^ñ rârôa ,¡,itlr r1r^ nnntract operator, but could be estimated from charter rate quotes.

No published source is available for other ground costs.

Administrative overhead and profrt estimates are also not

available from public sources, but could be estimated based

on general industry conditions or calibrated using current

rate levels. There cost items are often stated as a percentage

of all other costs.

The allocated unit cost per ton depends highly on the assumed load factor in both directions. Aircraft freight capaciry

for combination vessels can vary based on the service area

(affecting fuel requirements) and passenger load (e.g., baggage load). Operating capacities are available from various

aviation industry sources, as well as from the manufacturer. As

with vessel operations, it is critical that the impact of volumemeasured commodities on available capacity be considered.

The secondary status of air freight in the air transport industry is indicated by the lack of data and techniques for cost

analysis. While detailed unit costs a¡e available for passenger

hansport, freight costs are typically stated as general "per

pound" rates which are applied to entire markets (e.g., U.S. to

North Europe). Although integrated air carriers have detailed

internal costing methods, they do not file data that is comparable to that filed by passenger carriers and they use general tariff rates that are not easily correlated with specific traffic flows.

The following discussion addresses general cost elements

used in air passenger costing as applied to air freight operations for a combination or charter carrier. (The dedicated

closed systems of integrated carriers are not included in this

¡ U.S. Amy Corps of Engineers, Institute of Water Resources, FY 1995 Estimated

Towboat and Barge Cosl¡ D¡aft, Fort Belvoir, Virginia, no date.

5 Herbert Weinblatt, Goods-Movement Energy Eficiency: Overuiew, prepared by

Jack Faucett Associates for the California Energy Commission, Sacramenlo, November 1982, p.51.
130

APPENDIX G

RAIL/TRUCK

Modal choice generally is determined by minimizing perceived total logistics costs (TLC) for using the various modes

or modal combinations that are practical for a given set of

shipments. TLC consists of actual transport costs (or carrier

charges) incurred by the shipper plus a variety of other logistics costs (including inventory costs, stock-out costs, etc.)

incurred by the shipper or receiver. Any increase in TLC for

use of a particular mode can result in diverting some traffic

from that mode to competing modes, and any decrease in

TLC can result in diverting some traffic from competing

modes to the mode in question.

Potential modal diversion can be estimated using either

disaggregate data for a sample of potentially affected movements or more aggregate data in which the total volume of

such movements has been summarizedby one or more key

variables, such as by commodity. The diversìon estimates

can be derived from estimates of before and after TLC, from

absolute or percentage change in TLC, or, for situations in

which other logistics costs are essentially unaffected, from

changes in transport costs (or carrier charges) incurred by the

shipper.

Computer models that have been developed for performing disaggregate analyses ofraiUtruck diversion include: the

proprietary Intermodal Competition Model (ICM) developed

by the Association of American Railroads (AAR);t and the

recently developed Truck-Rail, Rail-Truck (T-R/R-T) Diversion Model developed by Transmode Consultants under contract to the Federal Railroad Administration.2 Brief reviews

of both models are presented in Appendix H. Although concerns about both models exist, the ICM has been used to estimate modal diversion in several public and proprietary studies. The T-R/R-T Model, on the other hand, is essentially

untested and, in its current form, apparently contains a significant number of questionable parameter values that are

likely to affect its results.

The first two sections of this appendix present some

sources of aggregate data that can be used for performing

modal diversion analyses when acceptable diversion models

are not available. The data are presented as elasticities of

MODAL DIVERSION

modal demand (in tons or ton-miles) relative to changes in

rail rates or truck costs. Truck costs are used (instead ofrates)

because they are more easily estimated (see Appendix F) and

because the highly competitive nature of the trucking industry causes trucking companies to pass both upward and

downward cost changes through to shippers in a reasonably

direct manner.

The concluding section of this appendix contains a more

technical discussion of the development of elasticities.

G.1 EFFECTS OF CHANGES IN TRUCK COSTS

Cross Elasticities from the ICM

One source of aggregate data for diversion analyses consists of a set of cross elasticities developed, by commodity

group, by Jones, Nix and Schwier,3 using results obtained

from the ICM. These cross elasticities are presented in

Exhibit G.1. Each cross elasticity represents the percentage

change in rail ton-miles that would result from a one percent

change in truck costs. For example, a one percent decrease in

truck costs would result in diverting to truck 2.0 to 2.2

percent of the ton-miles of food products currently carried

by rail.

Exhibit G.1 shows high elasticities (generally above 2.0)

for most categories offinished or highly processed goods and

much lower elasticities (below 1.0) for all categories of bulk

materials and for automobiles. Since rail traffic now consists

disproportionately of the latter categories of commodities,

the overall effect of changes in transport costs would be

sornewhat less than a glance at Exhibit G.1 might suggest.

It is reasonable to presume that the cross elasticities shown

in Exhibit G.l represent the effects of a reasonably uniform

change in truck costs;4 and they also can be used to analyze

the effects of a reasonably uniform change in rail rates (by

estimating the equivalent change in truck costs that would

have the same effect on the difference in costs for using the

two modes). However, somewhat different effects may be

' Scott M. Dennis, Iåe Intemodal Competition Model, Association of American

Railroads, Washington, D.C., September 1988.

2 Transmode Consùltants, lnc., Truck-Rail, Rail-Truck Diversion Model, Users

Manual, Draft, Washington, D.C., December 1994.

3 J. Jones, F. Nix, and C. Schwier, The Impact of Changes in Road (Jser Charges on

Canadian Railways, prepared for Transport Canada by the Canadian Institute of

Guided Ground Transport, Kingston, Ontario, September 1990,Table 4.2.

a The actual assumption used in the ICM analysis are not stated in the report and are

no longer readily available (Joseph Jones, Boon, Jones and Associates, personal

communication, November 1994).
Exhibit G.1. Implicit cross elasticities by commodity

group derived from ICM results.

Bulk Farm Products

Finished Farm Products

Bulk Food Products

Finished Food Products

Lumbera¡dWood

Fumiture

Pulp and Paper

Bulk Chemicals

Finished Chemicals

Primary Metals

Fabricated Metals

Machinery

Electrical Machinery

Motor Vehicles

Motor Vehicle Parts

Waste and Scrap

Bulk All Else

Finished AII Else

Commodity

Rall Ton-Mile

Cross Elasticities

131

The "Canadian Tax Policy" elasticity was developed from

results published by Jones, Nix, and Schwier. In this analysis, the Exhibit G.l elasticities were used to estimate the

effects ofpotential changes in Canadian truck-tax policy that

would decrease overall truck costs by four percent or

increase them by 9 or 17 percent. The Exhibit G.l elasticities

were applied, by commodity group, to traffic and revenue

data for the Canadian National (CN) and Canadian Pacific

(CP) railways. The elasticity shown in Exhibit G.2 was

derived by dividing the resulting estimate of the percentage

increase in rail ton-miles for the first policy alternative by the

assumed four percent decrease in truck costs. Some internal

inconsistencies in the Jones, Nix, and Schwier results6 leave

us with somewhat less confidence in this elasticity than in the

preceding set of elasticities.

The final four sets of cross elasticities were developed

from the results of three ICM analyses of potential changes

in U.S. truck size and weight regulations.T'8 All four sets of

cross elasticities relate the percentage change in rail

ton-miles and rail revenue to the average percentage change

in costs for all shipments carried by combination trucks.

The principal distinction between the four sets of size-andweight analyses are the assumptions relating to truck lengths.

The first analysis (labeled "Bridge Formula B") would allow

some increase in truck weights but would have very little

effect on lengths; the second analysis would also allow the

use of twin 33-foot trailer combinations on a relatively extensive set of major roads; and the last two would also allow the

use of twin 48-foot trailer combinations on the Interstate System and on some additional roads. The last two analyses

differ in their estimates of the amount of traffic that can be

carried efficiently on twin 48s. These two sets of diversion

estimates also were adjusted downward by Sydec to minimize the effects of some limitations in the ICM's ability to

represent the network on which twin 48s would be allowed

to operate.e

The Exhibit G.2 cross elasticities show substantial variation between the results obtained from different analyses.

The first two analyses assume a uniform change in costs for

all use of combination trucks, while the last four assume the

changes in truck costs are relatively concentrated on longer

haul truck movements that tend to be more competitive with

the rail industry. For example, in the "Bridge Formula B"

case, the average cost savings for all combination trucks was

estimated to be about one percent, but the savings for ship0.02

3.5

0.62

2.0

0.57

4.0

0.77

0.49

3.2

1.2

5-2

3.7

4.7

0.27

1.1

0.77

0.14

3.9

Source: J. Jones, F. Nix and C. Schwier, The lmpøct of Chønges in

Road User Charges on Cantdiøn Railways, prepared for

Transport Canada by the CanadianInstitute of Guided

Ground Transport, Kinpton, Ontario, September 199O

Table 4.2.

0.03

3.7

0.83

2.2

0.73

4.7

0.93

0.67

3.5

1.5

/.s

4.8

4.8

0.28

7.4

0.22

0.19

4.5

expected if a change in costs is not uniform (e.g., it affects

long-haul traffic differently than short-haul traffic or tank

trucks differently than vans).

An alternative to the use of elasticities by individual commodity group is the use of overall elasticities. Exhibit G.2

presents six sets of overall cross elasticities developed from

published results using ICM data. For each source, the elasticities show the effects of a one percent change in truck costs

on rail ton-miles and, for all but one of the sources, on rail

revenue. The estimated effects on rail revenue include revenue lost both as a result of losing traffic and as a result of

rate reciuctions aciopted in order io avoici aciciitionai traffic

losses.

Exhibit G.2 indicates that the effects on rail revenue are

always somewhat greater than the effects on rail ton-miles.

This is the case for two reasons: the most readily diverted

traffic (as indicated in Exhibit G.1) tends to be more highly

rated (cost more per ton-mile) than average; and some rail

revenue is lost on traffic that is retained by rail as a result of

rate reductions.

The first set of elasticities shown in Exhibit G.2 were

developed by Scott Dennis, of the AAR,s by using the ICM

to analyze the effects of a uniform ten percent reduction in

costs for all rail-competitive trucks.

5 Scott M. Dennis, op. c¡t-, pp.7-9.

6 Jones, Nix, and Schwier data actually can be used to derive both a rail ton-mile elasticity (1.00, as shown in Exhibit G.2) and a rail revenue elasticity (0.95). For ¡easons

discussed above, these elasticities are inconsistent with each other. Accordingly, to

avoid misleading the casual reader, the second elasticity has been omitted from Exhibit

G.2.

?Herbert Weinblatt, Modal Diversion Effects of Changes in Truck Size and Weight

Llmlts, Working Paper, prepued by Jack Faucett Associates for the Federal Highway

Administration, July 1990, Exhibit 5.

8 Sydec, Inc., Transmode Consultants, Inc., and Jack Faucett Associales, Analysis of

lnnger Combination Vehícles, Final Report, prepared for the U.S. Department of

Transportation, November 1993, Exhibits IV-4 and IV- I 1.

e lbid.,pp. V-4-V-5. AIso discussed briefly in Section H.l ofthis report.
r32

Exhibit G.2. Implicit overall cross elasticities from the

ICM.

1. Unifom Chanç in Truck Costst

2. Canadian Tax Policf

Size and Weight Analys€s

3. Bridge Formula É

4. Twin33s3

Twin,lSs¿

5. LowUsaç

ó. High Usage

Elasticities derived from:

lScott M. Dennis, The Intetmodal Competition Mod¿l, A,ssociation of

American Railroads, September 7988, pp.74.

t 

¡. ¡ones, F. Nix and C. Schwier, The Impact of Chang* in Road lJser

Chargæ onCanadìan Railways, prepared for Transport Canada by

the Canadian Instihite of Gu¡ded C,aourd Transport, Krngstory

Ontano, September 7990, p. 27.

3 Herb€rt Weinblatt, Modøl Dfuersion Effects of Changæ in Truck Size

anilWeíght Limifs, Working Paper, prepared by Jack Faucett

Associates for the Federal Highway Administration, July 1990

Exhibit 5.

a Sydec, Inc., Transmode Consr¡ltants, Inc., and Jack Faucett Associates,

Analysis of Loniger Combnatio¡ Vehicles, Fínal Report, prepated

for the U.S. Department of Transportatiory November 1993,

Exhibits IV-4 and IV-11.

ments that actually benefited from the higher limits was estimated to average 3.6 percent and to be as high as 14 percent

for some movements. The concentration of the cost savings

on relatively competitive operations results in greater diversion than would be produced by a more uniform distribution

of the cost savings.

The above discussion leads us to conclude that uniform

changes in the cost of operating combination trucks are likely

to produce cross elasticities of about 0.5 for rail ton-miles

and 0.8 for rail revenue, and that changes that are more

focused on rail-competitive segments of the truck industry

are likely to produce cross elasticities that are two to three

times as large.

Cross Elastic¡t¡es from the CN and CP

Rail Ton-Miles R¡il Revenue

C¡oss Elasticities

0.52

1.00

tion in truck costs for the three scenarios (which ranged from

8 to 14 percent), Jones, Nix, and Schwierrr derived the

implicit cross elasticities shown in Exhibit G.3.

The CP diversion estimates tended to produce slightly

larger cross elasticities than the CN estimates. More significantly, both sets of cross elasticities are appreciably smaller

than those produced by the ICM for the effects ofchanges in

truck size and weight limits. At least part of the reason for the

lower cross elasticities is that the Canadian railroads have relatively large volumes of long-haul movements of low-value

natural resources-commodities that, as indicated in Exhibit

G.1, have relatively low cross elasticities and are relatively

resistant to diversion to truck. Other possible contributors to

the difference in cross elasticities could include tendencies for

the CN and CP analysts to have underestimated diversion or

for the ICM to have overestimated it.

Conclusions

0.99

1.50

2.@

2.30

o:tt

7.43

2.30

2.43

2.91

On the basis of the above discussion, we conclude that, for

uniþrm changes in truck costs, it is appropriate to assume

cross elasticities of about 0.5 for rail ton-miles and 0.8 for rail

revenue.

Separate cross elasticities were not obtained for rail tons.

However, most rail traffic diverted to truck is likely to be

intermodal traffic, frequently moving long distances, or single carload traffic, most typically being shipped more moderate distances. (Most short distance single carload shipments have already been diverted to truck while the longest

haul movements are more insulated from truck competition

than more moderate-haul movements.) Therefore, the length

of haul of newly diverted rail traffic is likely to be slightly

higher than average, and the cross elasticity of rail tons is

likely to be slightly smaller than that of rail ton-miles. Hence,

it would appear appropriate to assume that for a uniform

change in truck costs, the cross elasticity of rail tons is likely

to be about 0.4.

For changes in truck costs that are concentrated on the

more rail-competitive truck operations, when expressed relative to the average change in costs for combination trucks,

the cross elasticities are higher. In the case of the truck size

and weight studies reviewed, the cross elasticities ranged

from 1.0 to 2.3 for rail ton-miles and from 1.4 to 2.9 for rail

revenue. Accordingly, for nonuniform changes in the cost of

operating combination trucks, some judgment is necessary to

determine the extent to which the changes are focused on

rail-competitive truck operations, and so the extent to which

the cross elasticities suggested in the preceding paragraph

should be increased.

Since rail routes usually are more circuitous than truck

routes, the change in truck ton-miles generally will be

smaller than the change in rail ton-miles. Estimates of the

Another source of cross elasticities is a set of modal diversion estimates developed by the CN and CP railroads as part

of a 1981 study sponsored by the Roads and Transportation

Association of Canada.l0 In that study, the two railroads provided estimated ranges for the expected effects ofthree possible changes in truck size and weight limits on their traffic

volume and revenue. Using estimates of the average reducr0 N.A. Irwin and R.A. Barton, Economícs of Truck Sizes andWeights in Canad.a,

Final Report, Council on Highway and Transportation Research and Development ând

the Roads and Transportation Association ofCanada, Ottawa, July 1987. rr Jones, Nix, and Schwier, ap. cit.,TabIe 4.3.

l

ì.
Exhibit G.3. Implicit cross elasticies from CN and CP

analyses.

Canadian National

Canadian Pacific

Source: J.Joræs,F.Nix, andC.Scwier, The Impact of Changes inRoad

User Charges on Canailian Railways, prepared for Transport

Canada by the Canadian Institute of Guided Ground Transport,

Kingston, Ontario, September 1990, Table 4.3.

change in truck ton-miles can be obtained by multiplying the

rail estimate by -0.85.''?

G.2 EFFECTS OF CHANGES IN RAIL RATES

AND RAIL COSTS

Rail Ton-Miles Rail Revenue

Cross Elasticities

0.39 - 0.51

0.35 - 0.59

Information about the modal-diversion effects of changes

in rail rates and costs is less readily available than the information presented above about the effects of changes in truck

costs.

Much traffic currently canied by rail is fairly well insulated from intermodal competition, though the portion of rail

traffic that is not well insulated is somewhat larger than the

coffesponding portion of traffic in combination trucks

(which includes substantial amounts of local and short-distance movements). Railroads usually have a substantial

advantage in efficiency for transporting multi-carload shipments, and such shipments constitute aboú62 percent of rail

tonnage.13 Because rail traffic is somewhat less well insulated from intermodal competition than truck traffic, uniþrm

percentage changes in rail rates are likely to result in diverting somewhat more traffic between modes than would the

same unifoÍn percentage change in truck costs and rates.

In the preceding section it was suggested that a uniform

one percent change in truck costs would result in diversion

amounting to about 0.5 percent of raii ion-miies anci 0.4 percent of rail tons. The above discussion implies that a uniform

one percent change in rail rates might result in diversion

amounting to about 0.75 percent of rail ton-miles and 0.6 percent of rail tons; i.e., that the own elasticities of rail ton-miles

and rail tons to changes in rail rates are about -0.75 and

-0.6 respectively. (These elasticities are negative since an

increase in rail rates will result in a decrease in rail traffic.)

The elasticities suggested in the preceding paragraph are

appropriate when changes in rail costs and rates are reasont33

ably uniform across all categories of traffic. Changes in rail

costs that apply primarily to truck-competitive traffìc (most

of which provides relatively low operating margins) are

likely to produce rate changes that are concentrated on

this traffic. As in the case of changes in truck costs, rate

changes that are concentrated on modally competitive traffic

are likely to produce substantially higher elasticities than

uniform changes in rates, with the highest elasticities

(perhaps in the two to four range) likely for doublestack and

trailer-on-fl atcar traffic.

As suggested in the preceding section, changes in truck

ton-miles can be derived by multiplying estimated changes

in rail ton-miles by -0.85.

G.3 FREIGHT DEMAND ELASTICITY STUDIES:

TECHNICAL CONSIDERATIONS

Three different methods are commonly used in the

transportation literature for computing elasticities: ra

0.5,1- 0.71

0.59- 0.92

. A point elasticity is calculated by

quantity demanded as a function of

calculating

,.. = lgL " dP8

If the functional relationship between quantity and price is

not available, then it is generally not possible to calculate a

point elasticity.

. An erc elasticity is calculated from information on price

and quantity before and after a price change

logQ, - logQ

"': lo¡Pr-logn

This measure most nearly approximates a point elasticity.

. A shrinkage factor also is calculated from information

on price and quantity betore and after a price change

@, - 8,)lQ,

"" = 

çp, - e,v r,

The problem with calculating elasticities as shrinkage factors is that if the price is reduced by a given amount and then

increased by the same amount, the shrinkage factor does not

predict that demand will return to its original level. For small

changes in price, however, the shrinkage factor will not

differ significantly from the arc elasticity.

Elasticities can be short-run or long-run in nature, depending on the time period over which changes in demand are

r2 The results of two ICM analyses indicate that, on average, rail routings are I 6 to I 8

percent more ci¡cuitous than truck routings. (Herbert Weinblatt, Modal Diversion

Effects of Changes ínTruck Size and Weight Limits,Working Paper, prepared by Jack

Faucett Associates fo¡ the Federal Highway Administration, July 1990, Exhibit 4.)

lr Derived from 1992 waybill data from Bu¡eau of Transportation Statistics, Ra¡l

'tilaybill Data: I9I8-I992, CD-ROM, Washington, D.C., |994.

expressing the

price and then

'' Barton-Aschman Associates Inc. and R.H. Pratt, Traveler Response to Trqnsportation System Changes, prepared. for the U.S. Department of Transportation, Federal

Highway Administration, July 1981.
134

observed. Differences between short-run and long-run elasticities can be substantial. For example, the short-run price

elasticity for gasoline is about -0.20, while the long run

elasticity is close to * 1.00. This is because in the short-term,

the only way to reduce gasoline consumption is to reduce vehicle miles of travel, while in the long term, more

fuel-efficient vehicles can be used.

The focus of this section is on using price elasticities of

demand to measure the responsiveness of demand to a

change in price. Analysts employ elasticities to evaluate how

proposed policies will impact freight demand. Ordinary price

elasticities of freight demand include the scale or output

effect associated with a change in price-i.e., they assume a

carrier might adjust output levels as part of an overall

response to changes in prices. In contrast, conditional or

compensated elasticities measure the substitution effects of a

price change and hold output constant. In their study of

freight demand elasticity models, Oum, et. al., found that

many of the existing empirical models do not treat output as

an endogenous variable and, as a result, may report biased

elasticities.l5

Since compensated elasticities are the only elasticity measures available, they are used by policy analysts in assessing

impacts of proposed policies. Fortunately, in most applied

planning situations, the analyst is most critically interested in

the modal substitution issues-i.e., an estimate of how much

traffic will be shifted from one mode to another as a result of

a given price change in one of the modes. The compensated

elasticity models provide estimates of the specific measures

of interest to the analysts.

There have been two major approaches in collecting the

data needed for developing estimates of freight demand elasticities. One involves the collection of aggregate data to

develop elasticity estimates. Specifically, these studies rely

on data collected in the aggregate for a particular geographic

corridor (state-to-state freight shipments, for example) on

average freight rates, shipment volumes, shipment times, and

delivery reliability by mode. Depending upon the particular

analytic model used to develop elasticity measures, the

dependent variable will vary to some degree. If a logit model

is used to develop elasticity measures from the aggregate

data, the dependent variable is a ratio of the modal volumes.

If the translog model is used, the dependent variable is average freight rate on the geographic corridor for all modes.

Oum analyzed a variety of functional forms for developing

elasticities from aggregate data and concluded that the

translog model performed the best in all aspects.r6 When the

policy analyst is focusing on total traffic volumes and modal

shifts occurring as a result of the implementation of a particular policy, then elasticities from aggregate models seem

most appropriate.

The second class ofmodels requires researchers to collect

data from a representative sample of individual shipments.

For each shipment, information is collected on freight rates

for the mode used as well as alternatives. Data are also accumulated on time and variability of the shipment by the mode

used and the alternative. In most instances, some form of

logit or probit model is employed to develop freight demand

elasticities from the disaggregate data.rT The disaggregate

models have intuitively appealing features. For example, the

decision to shift traffic from one mode to another as a result

of shifts in price is, in essence, an individual, disaggregate

decision. It is more logical to use disaggregated data from

individual shippers to develop elasticity measures designed

to capture such individual decisions. Winston concludes:

"(disaggregate models) offer a much richer econometric

specification than any of the previous freight demand models. In addition, disaggregate models yield more precise estimates of market elasticities than the a91regate or inventory

models. Finally, and perhaps most important, the disaggregate models are grounded in abehavioral theory ofthe actual

decision-maker's behavior thereby adding considerable

substance to any policy implications."rs

However, despite these advantages, the development of a

disaggregated data base is problematic. For one, it is time

consuming and expensive to develop. Second, many of the

data items required are not easy to obtain because of the confidentiality of private information.re Third, there is always

the issue of whether the selected sample is representative of

all major groups in the universe as well as the issue of

expanding the sample results to the universe. Ifthere are any

biases in sampled shippers, these biases will be reflected in

the developed elasticity measures. Use of the disaggregate

data require the analyst to spend a great deal of time developing a systematic approach for its expandibility to the

population.

The issue here, however, is not so much on whether the

disaggregate or aggrcgate approach is the most time and cost

efficient, rather the issue here is to find a set of elasticities

that can be employed when an analyst wishes to estimate the

demand impact of modal coslprice increases resulting from

the initiation of a new policy. As Oum has argued, the disaggregate and aggregate approaches should be viewed as

complementary, not competing.2o

The analyst must be aware of the limitations in the entire

set of freight demand elasticities that have been developed

15 Tae Hoon Oum, W.G. Waters II, and Jong-Say Yong, "Concepts of Price Elasticities of Transpon Demand and Recent Empirical Estimates," Joumal of Transport

Economics and Policy,May 1992, p. 142.

ró Tae Hoon Oum, "Altemative Demand Models and Their Elasticity Estimates,"

Joumal ofTrønsport Economics and Policy,May 1989, p. I 85.

17 Clifford Winston, "A Disaggregate Model of the Demand for Intercity Freight

Transportation," Econometrica, Vol.49, No.4 (July 1981), p. 981.

¡8 Cliffo¡d Winston, "A Disaggregate Model of the Demand for Intercity Freight

Transpofarion," .Econometrica,Yol.49, No. 4 (July 1981), p. 998.

re Both Oum in "Altemative Demmd Models and Their Elasticity Estimates," and

'Winston in "A Disaggregate Model of the Demand for Intercity Freight Transportation" discuss the advantages and disadvantages of the disaggregate models.

20 Tae Hoon Oum, "Altemative Demand Models and Their Elasticity Estimates,"

op. ctt., p.164.
in the literature. This section discusses some of these

limitations.

For one, the most careful and thorough freight demand

elasticities developed to date do not reflect all the changes

that have occurred in the freight transport sector since deregulation. There were a flurry of very impressive freight

demand elasticity studies conducted in the mid to late 1970s.

However, there has been a dearth of such studies in the

deregulated environment.

The absence of such investigations reflects a number of

factors. Deregulation has shifted the focus in rate-making

from the collective group to the individual carrier and shipper. More and more rates are being negotiated between carriers and shippers and kept out of the public domain. For

example, more and more of the records from the Railroad

Waybill Data do not include rate information because the

rates are negotiated between the parties and filed as "contract

rates." There is no question that the transport sector has

changed dramatically since passage of the Motor Carrier Act

of 1980 and the Staggers Rail Act of 1980. As a consequence,

reference to elasticities based on pre-1980 data must be

subjected to careful scrutiny.

There are, for example, solid data supporting the growth

of intermodal transportation in the new deregulated environment. Railroads have experienced unprecedented growth in

intermodal traffic throughout the 1980s and into the 1990s.

All indications point toward higher and higher levels of intermodal movements on this nation's railroads. Certainly, the

development of the intermodal option is now much more

135

prominent in the ma¡ketplace than it was prior to deregulation. Demand elasticities not reflecting these changes must be

carefully interpreted.

As noted above, there are limitations inherent in both the

ag$egate and disaggregate approach to the development of

demand elasticities. The aggregate studies suffer from their

inability to model the actual modal decision process. All

individual decisions are lumped together and the unit of

analysis becomes modal market shares or freight rates on

particular corridors. The disaggregate studies are based on a

limited set of individual decisions which may be taken out of

context from a shipper's overall modal assessment process.

Thus, shippers might make modal decisions based on their

entire set of shipment needs over a particular time periodquarter, half-year, or year. It may not be very effective to

base demand elasticities on an individual shipment from a

particular shipper rather than from that shipper's entire set of

shipments.

Despite these very significant limitations, the policy analyst still must address the issue of how to estimate freight

demand impacts associated with the implementation of a policy with a quantifiable impact on modal costs. The next section will present freight demand elasticity estimates that represent the best available estimates. There will be strong

caution that these estimates have some very significant limitations. Nevertheless, the policy analyst may need to evaluate them as the best available evidence, albeit evidence that

must be carefully screened, evaluated, and subjected to

sensitivity analysis based on additional available evidence.
r36

APPENDIX H

TI-I REE MODAL.DIVERSION MODELS

This appendix contains brief descriptions of two computer

models that are designed to estimate modal diversion

between rail and truck using disaggregate data on individual

shipments and a third model designed to estimate diversion

using more aggregate data.

H.1 INTERMODAL COMPETITION MODEL

The most commonly used tool for estimating rail/truck

modal diversion from disaggregate data is the AAR's proprietary Intermodal Competition Model (ICM).' This model

is designed to analyze a sample of actual rail movements,

taken from the ICC Carload Waybill Sample,2 and, for these

movements, to estimate which will be diverted to truck,

which will be retained as a result of competitive railroad rate

reductions, and which will be unaffected by the reductions in

truck transport costs. The most recent version of this model

also is capable of analyzing the effects of increased truck

costs on railroad rates charged on existing truck-competitive

rail movements and on diversion from truck to rail (using

a sample of truck movements from the North American

Trucking Survey).3

The proprietary nature of the ICM makes a careful evaluation of the accuracy of its estimates difficult. We have

reviewed output produced by the previous version of the

model and concluded that the cross-elasticities of rail

demand relative to changes in truck costs that are implicit in

these results appear to be reasonable.a However, the comparison of cross-elasticities produced by the ICM to those

produced by a CN/CP analysis discussed in Appendix G suggests that the ICM may tend to overestimate diversion moderately. For this reason, caution should be applied when

using results produced by the model.

An important concern about the use of the ICM relates to

the truck cost analysis performed by the model. This analysis presumes that the utilization rates of larger and heavier

vehicles generally would be the same as current utilization of

48-foot semis; i.e., that all loads carried would be loads for

which the vehicles are designed and that there would be no

increase in empty mileage and no decrease in annual

mileage. These assumptions about utilization are optimistic,

especially with respect to non-door-to-door configurations

such as twin 48s. The ICM's estimates of cost savings resulting from the use of larger and heavier trucks are overstated,

and, accordingly, modal-diversion estimates derived using

these cost estimates are too high. This problem is not insurmountable. The model has been run in the past using exogenously specified estimates of the effects of regulatory

changes on truck transport costs;s and adjustments also can

be made to ICM results (with some loss of accuracy) to compensate for any known tendency of the model to over or

underestimate diversion.

Several other factors have affected ICM results that have

been produced in the past, although some of these may have

been corrected in the latest version ofthe model. These factors are listed below, along with estimates of the effect of

these factors on the model's estimates of overall diversion to

twin 48s.6

. Fuel taxes were assumed to be zero on all truck movements originating in Canada, increasing overall diversion by an estimated 8.0 percent in the twin-48 analysis

and to an unknown extent in other model runs.

. The costs of reconûguring twin 48s and the costs of

access hauls to the twin-48 network were not adequately

reflected for short hauls (particularly those under 800

miles) while they were overestimated for long hauls

(particularly those over 1,800 miles), increasing overall

diversion by about 23 percent.

. Because the ICC waybill sample does not identify the

true origin and true destination of intermodal movements (but only the rail origin and rail destination), the

ICM underestimates the cost of intermodal movements

and significantly underestimates diversion of these

movements. Overall diversion was estimated to be

reduced by 3 percent, but the magnitude of this underestimate can be expected to grow as intermodal traffic

grows. I Scott M. Dennis, Tl¿e Intennod.al Competitiott Mod.el, Association of American

Railroads, Washington, D.C., September 1988.

2 The ICC Carload Waybill Sample consists of a systematic sample of waybills for

railroad shipments terminating on Class I railroads in the United States.

3 The North Amedcan Trucking Survey is a survey of truck drivers conducted during

1993 and 1994 af 46 truck stops by Afthu D. Little, Inc., under contract to the

Association of American Railroads.

4 Herbert Weinblatt, Modal Diversion Effects of Changes in Truck Size and Weight

lirnirs, Working Paper, prepared by Jack Faucett Associates for the Federal Highway

Administration, Washington, D.C., July 1990.

s lbid.; and Sydec, Inc., Jack Faucett Associates, and Transportation Consulting

Group,Inc.,TmckSi¿e andWeight and User Fee Policy Analysis Study, Part One: Productívity Effects of Policy Options,Final Report, prepared for the Federal Highway

Administration, Washington, D.C., March 1991.

6 Sydec, Inc., Transmode Consultants, Inc., and Jack Faucett Associates, Analysis o/

Innger Combination Vehicles, Final Report, prepared for the Federal Highway

Administration, Washington, D.C., November 1993, pp. V-4 and V-5.
. The use of waybill data for a recent historic year tends

to understate the portion of rail traffic that is intermodal

or will be in the future. Since intermodal traffic is the

traffic most readily divertible to twin 48s, this understatement tends to reduce overall diversion to twin 48s.

. The ICM estimates of other logistics costs (OLCs)

(which have been printed in the past but are no longer

printed) do not appear to represent realistic relationships

between OLCs for rail movements and OLCs for truck

movements. (However, the model appears to have been

calibrated to compensate for this effect.)

It should be emphasized that some of these problems may

have been corrected in the latest version of this model.

Also, a slight bias toward understating diversion occurs

because this model, and most diversion models, estimate

only the direct effects ofan exogenous change in costs. Some

additional effects, not measured by the model, may result as

traffic is diverted from one mode to another, decreasing the

economies of density and increasing costs for the first mode,

and having the opposite effect on the second mode.

Finally, no review has been conducted of the construction

of the North American Trucking Survey (NATS) or of the way

the ICM uses this data to represent the universe of railcompetitive truck shipments. However, the National Motor

Truck Data Base (the predecessor to the NATS) had an inherent, but easily conectable, bias toward overrepresenting longhaul movements.T If the ICM is used with NATS for estimating diversion from truck to rail resulting from policy changes

that increase truck costs or reduce rail costs, a failure to adjust

for this bias will result in significantly overrepresenting longhaul truck movements, which are relatively divertible, and so

in overestimating diversion from truck to rail.

H.2 THE T-F/R-T DIVERSION MODEL

137

used, but those of intermodal shipments are not. The model

creates assumed origins and destinations for these shipments

from their intermodal origins and destinations, County

Business Pattern data, and a gravity model.

The T-R/R-T Model estimates origin/destination (O/D)

distances for conventional truck movements as great-circle

miles (GCMs) between county seats, adjusted for circuity.

For LCV movements, the model estimates mileages of LCV

operation from a nodelink representation of an LCV network and from mileages of access hauls using GCMs

between origins and destinations and nearby LCV network

nodes (assumed to represent staging areas). The model

currently assumes that LCVs can operate on all ramps

connecting LCV network links.

For shipments that currently are not handled by conventional rail, railroad O/D distances are estimated by applying

a raiVtruck circuity factor to GCMs. It is not clear what

assumptions are made about the availability of rail service at

the origin and destination. The use of a rail/truck circuity factor results in consistent estimates of rail and truck O/D distances (both of which apparently are underestimated as a

result of omitting any adjustment for truck/GCM circuity).

For shipments that are currently handled by conventional

rail, railroad O/D distances are set to actual distances

obtained from the railroad waybill. The use of actual distances for rail and GCMs with no circuity factor for truck

results in overestimating the difference in length of haul

between the two modes and biases the analysis toward railto-truck diversion.

AII intermodal shipments are assumed to be made through

one of 32 major intermodal rail terminals at each end of their

rail haul. Rail distances between each pair of these terminals

are maintained in a matrix used by the model and are actual

rail distances between terminals. The use of a restricted set

of intermodal terminals most likely results in overestimating

highway access miles to intermodal terminals for some

shipments.

A major advantage of the T-RiR-T Model relative to the

ICM is tha.t the T-R-/R--T Model rs nonproprietar,v. The T-Tsers

Manuale provides a better description of the model and its

construction than available documentation for the ICM.

However, no definitions or derivations for the many parameters incorporated in the model are provided (though some of

the parameter values can be inferred from three pages of output reproduced in an appendix); and the Users Manual

provides no information about how to modify any of these

parameters.

A second advantage of the T-R/R-T Model is its ability

to create initial origins and final destinations for current

intermodal movements. This capability enables the model

to develop much better estimates of the potential for diverting current intermodal movements to alternate modes than

the ICM was able to do the last time we were exposed to

its use for this purpose (as discussed in the preceding

subsection).

The Truck-Rail, Rail-Truck (T-R/R-T) Diversion Model is

a new model currently being developed by Transmode Consuitants under contract to the Federal Railroad Administration (FRA). A preliminary description of this model is contained in a Draft Users Manuals released in December 1994.

The actual model and a somewhat revised Users Manual are

scheduled for release in the next few months.

The T-R/R-T Model is based on much of the same

research as the ICM. It distinguishes four types of truck

transport (truckload (TL), Iess than truckload (LTL), longercombination vehicle (LCV), and private); three types of

intermodal transport (trailer-on-flatcar, doublestack, and

Road Railer); and conventional rail carload transport.

The T-R/R-T Model represents nearly all movements as

originating and terminating at county seats. The actual origins and destinations of shipments currently being made by

truck or conventional rail are contained in the data sources

1 lbíd., p. C-7 .

8 Transmode Consult¿ìnts, lnc., Truck-Rail, Rail-Truck Diversion Model, tJser

Manual, Draft, Washington, D.C., December 1994. , Ibid
138

Despite these advantages, several concerns exist about the

current version of the T-R/R-T Model as a result of a brief

review of the draft model descrþtion and of the three pages

of output produced in an appendix for a single shipment (of

a weight-limited sodium compound).

The most significant concerns relate to the analysis of

LCVs. Data contained in the appendix indicates that transit

times for LCVs are assumed to be one-third shorter than

those of for-hire TL transport, and that reliability is assumed

to be 20 percent better. Although not discussed anywhere in

the Users Manual, the shorter transit times reflect an assumption that around-the-clock relay operation would be used for

LCVs but not for conventional trucks. However, the cost

structures used for LCVs and for conventional trucks apparently do not reflect any cost difference between relay operation and the single-driver operation assumed for conventional trucks. (If the costs actually are similar, conventional

TL operators would choose to provide the better service

attainable with relay operation.)

The transit time assumption for LCVs apparently also

ignores the delays that can be expected at staging areas in

order to match pairs of trailers moving in the same general

direction. Also, because of the need for such delays (without

which the economies of LCV operation are unattainable), it

seems that, for most shippers, transit-time reliability of LCVs

would be poo rer than that of conventional truckload service

(though some shippers might be willing to pay a premium to

guarantee expedited handling of their trailers).

Other concerns include:

. The procedures used for estimating length of haul for

shipments currently handled by rail (discussed above)

apparently overstate somewhat the lower circuity of

truck, thus biasing the analysis somewhat toward

diversion to truck.

. A load ratio (loaded miles per total mile) of 1.0 is

assumed for all modes except rail (for which it is 0.6)

and private truck (for which it is 0.5). An overall load

ratio of 1.0 is unattainable for any mode. (There might

be some analytic justification for treating loaded backhauls as if they had load ratios of 1.0, or even higher; but

the movement in question-from Barstow, California to

Swansea, Illinois-is unlikely to represent a backhaul.)

. The assumptions used for LCV access costs (roughly

half to two-thirds of those for intermodal access costs)

may be somewhat optimistic.

. Rail costs appear to be modeled as being directly

proportional to distance, with no additional costs for

pickup and delivery.

. A negative charge for pickup and delivery appears to be

incorporated into the rate structure of truckload carriers

(actually, a $162 charge per shipment for pickup and a

$332 credit for delivery).

. The costs for LCVs appear either to exclude or to underrepresent the cost of reconfiguring LCVs en route and

the inefficiency resulting from an inability to pair all

trailers operating on the LCV network. Also, the apparent assumption that efficient interconnections will exist

between all intersecting LCV roads without any added

circuity will result in underestimating the lengths of

LCV hauls.

It is likely that some of these concerns will be addressed

prior to public release of the model. However, addressing

other concerns will require a larger effort than the one that

is currently underway. Accordingly, we are not confident

that the first version of the model will be appropriate for

analyzing modal diversion.

H.3 THE l,OOO.MILE STRATEGIC CHOICE

MODEL

The 1,000-Mile Strategic Choice (TMSC) Model currently is being used by Mercer Management Consulting

(MMC) to perform truck/rail modal diversion analyses as a

part of a study being conducted for the Southern California

Association of Governments (SCAG). The model is proprietary and very little information is currently available. However, because of its current use in an important public-policy

study, it warrants some brief discussion based on the limited

published description that is available.t0

Unlike the models described in the two preceding sections,

the TMSC Model apparently does not contain a representation of either the rail or highway systems. Instead, the model

focuses its analysis on the effect on modal choice of changes

in four modal characteristics. The modal characteristics considered are: transport costs, transit time, service reliability,

and accuracy of freight bills. The modes analyzed are truck,

rail intermodal, and rail carload. The effects of any policy

change on any of the modal characteristics apparently must

be specifi ed exogenously.

The model's estimates of the diversion effects of

changes in modal characteristics are derived from 1 17

responses to a survey of major shippers conducted in l99l

by Temple, Barker, and Sloane (MMC's predecessor).

Most or all respondents appear to be manufacturers, and

the relevant survey questions all focused entirely on shipments moving about 1,000 miles (hence, the name of the

model).

The survey used a very small sample. More importantly,

the information collected appeaß to be too naffow to be used

as the basis for estimating overall modal diversion. In particular, it is not clear what assumptions the model makes about

shipments of natural resources or about hauls that fall outside

of the 800 to 1,200-mile range.

I0 Mercer Management Consulting, lnterregioml Goods Movement Study,TaskZC

Report: Evaluation of Key Methodologies, prepared for the Southem California

Association ofGovemments, Los Angeles, April 1995.
APPENDIX I

CASE STUDIES

This appendix presents two case studies demonstrating the

practical application of some of the procedures presented in

this Guidebook.

The first case study presents the development of forecasts

of freight traffic for North Carolina's two existing freight airports and for a third proposed freight airport. These forecasts

make use of the comparison and proximity/level-of-service

procedures presented in Chapter 4 under Evaluating Proximity and Level of Service, and Analyzing Total Logistics

Costs of Individual Shipments.

The second case study presents an analysis of the effects

of a possible change in federal truck size and weight policy.

This case study demonstrates some of the cost estimation and

modal-diversion procedures presented in Appendices F-H.

1.1 CASE STUDY: NORTH CAROLINA

FREIGHT AIRPORTS

h L99l, the State of North Carolina's Department of

Transportation commissioned a study to evaluate short and

long term needs for the state's air cargo infrastructure.l The

study included an inventory of the state's air cargo facilities

and intermodal linkages, an analysis of system capacity, and

traffic forecasts to 2010 for state airports. The study also

evaluated the technical and market feasibility of the Global

Air Cargo Industrial Complex (GACIC) concept which is

currently being developed as the Global Transpark in Kinston, North Caroiina. The GACIC anaiysis inciudeci a projection of new industrial activity attracted to the facility and

the development of a forecasting model which allocates

future demand among the state's primary cargo airports

including various locations for the GACIC. This case study

examines the demand forecasting techniques used for both

the current airport system and one that included the GACIC'

Problem Definition and Research Objectives

The forecasting elements of this study required long-term

forecasts for existing cargo airports in the state with an

emphasis on the primary facilities at Charlotte (CLT),

Raleigh-Durham (RDU), and Greensboro (GSO). These

forecasts were required to determine the adequacy of existing and projected infrastructure. The GACIC portion of the

study required the ability to define a new cargo airport with

an indefinite location and capacity, also identifying new

industrial activity to be attracted to the aþort.

Air cargo demand forecasts for airpofs have traditionally

been based on trend analysis, projecting future growth based

on national trends and a continuation of historical growth. A

primary reason for this strategy has been the limited availability of data beyond airport traffic statistics. This approach

treats individual airports as independent of the larger markets

in which they actually compete. This study incorporated a

more detailed representation of the air cargo market, incorporating regional demand and ma¡ket share analysis. The

reasons for this more detailed analysis included:

. a requirement to forecast flows among multiple airports

which share a conìmon hinterland;

. a requirement to test various scenarios for the location

and service profile for the GACIC; and

. a requirement to identify specific industries which

might be attracted to a GACIC facility.

The following sections discuss the techniques utilized in

generating the demand forecasts.

Market Gharacteristics

The process for forecasting demand in this case included

measuring baseline activity and relationships and projecting

them into the future under various development scenarios'

The North Carolina air cargo market analysis isolated four

primary areas of data and activity:

. Market Demand;

. Airport Traffic and Aircraft Activity;

. Cargo Routing Patterns; and

. New Industrial Activity for GACIC.

The characteristics, sources, and techniques used to describe these market elements are discussed in the following

sections.

t39

I North Carolina Air Cargo System Plan and a Globql Air Cargo Industriol Complex,

by Transportation Management Group, Inc., Leeper, Cambridge & Campbell, lnc., and

COMSIS Corporation, February 1992.
140

Market Demand

Market demand was defined by the volume, location, and

type of air cargo shipments and receipts without regard for

airport routing. The characteristics of market demand are

shown in Exhibit I.1.

The geographic market definitions were determined by:

. a requirement to identify sub-state cargo flows;

. the ability of cargo airports to attract traffic from local,

regional and national markets with closer origins and

destinations more susceptible to capture; and

. the levels of detail available for various data sources.

Since all of the state's airports were included, the "local"

market was defined as the entire State of North Carolina at

the county level. A primary data source for cargo demand

was the Colography Group's estimates of domestic and

export air shipments generated by the top 73 manufacturing

industries (defined at the four-digit SIC level) by U.S.

county.2 The "regional" market was defined as an aggregation of various "airport market areas" which are county

groupings surrounding primary airports as defined by Colography. The "All Other U.S." market was defined as the rest

of the national market as measured for all airports.

The primary technique required for the geographicalbased data was associating the detailed county-based data

with more aggregated data at the state or other levels. For

example, the employment forecasts of the Bureau of Economic Analysis (BEA) are only available ar rhe two-digir SIC

industry level by state with more aggregate data available for

BEA regions (larger county groups associated with major

metropolitan regions) which did not match the Colography

regions. In most cases, detailed county-based characteristics

were assumed to mirror the average of data available for the

larger regions.

As market proximity is a key factor in determining air

cargo routings, the location of market origins and destinations relative to the study's airports was incorporated as highway distances between the airport and the "centroid" of the

local and regional market a¡eas. This geographical structure

easily allowed the introduction of "new" airport locations as

required in the GACIC analysis.

The total outbound market was estimated from the 73

industry totals using expansion factors provided by Colography as determined from national traffic totals. Inbound traffic

was estimated based on flow characteristics for the state's airports and assumptions based on outbound distributions. The

"All Other U.S." demand totals were calculated as the

residual of national totals minus the regional market estimates.

The forecasting methodology utilized for market demand

was designed to reflect the following characteristics of air

cargo markets:

. Air cargo traffic represents a segment of larger manufacturing, trade and transportation markets. Market

growth will incorporate national, regional and local economic trends. Employment growth trends were used to

represent the general growth in regional outbound shipments. BEA employment forecasts for state industry

groups and BEA county regions3 were combined and

modified for this purpose.

. The use of air cargo services relative to other modes has

increased significantly due to the implementation of

advanced distribution systems for both manufacturing

and consumer markets (e.g., just-in-time) and the trend

toward more globalized markets. The shift of the U.S.

industrial base away from traditional heavy industry

toward high technology manufacturing and service

industries has also resulted in a trend toward more air

service use. Historical Colography data for average air

cargo production per employee (in pounds) was compared for 1983 and 1990, generating average productivity growth rates used in the forecasts for regional

outbound shipments.

. Regional growth for outbound shipments was compared

with national growth as projected in the Boeing Company's World Air Cargo Forecaslsa resulting in traffic

projections for the "All Other U.S." category.

. Inbound traffic estimates assumed the baseline distribution by market region to national totals based on the

Boeing growth trends.

Airport Trffic and Aircraft Activity

The most common form of transportation data involves

facility statistics for ports, airports or border points. The

major drawback with most facility data is the lack of detail

regarding the origin and destination of traffic and through

routing information. This study attempted to correlate airport

traffic volumes with the underlying demand and supply markets in order to produce more results which represented the

underlying market relationships.

Baseline activity for North Carolina airports was derived

from published carrier statistics modified and supplemented

with information gathered in an interview program with airports, carriers and other air cargo firms. State airport traffic

was then compared with national traffic totals (as estimated

from the market demand totals). Exhibit L2 summarizes the

characteristics measured.

Total state traffic combines airport statistics published by

the Federal Aviation Administrations (for U.S. carriers), the

Airport Operators Council International6 (AOCD, and the

'?The Colography Group, U.S. Air Freight Origín Traffic Statistics, Marietta, Georgia, annual.

3 U.S. Department of Commerce, Bureau of Economic Analysis, BEA Regionat

Prcjections 1o 2040, U.S. Govemment Printing Office, October 1990.

a Boeing Commercial Aiçlane Group, World Air Cargo Forecast, Seattle, annual.

5 Federal Aviation Administration, Airport Activ¡ty Status of Certifcated Air

Carriers, anntal.

6 Airport Operators Council Intemational, Worldwide Airport Trafrc Report, annùal.
Exhibit I.1. Market demand characteristics.

Geographic Regions

Data Item

Airport-Market Distances

Outbound Air Shipments by

Industry Group and Origin

Market Area

Inbound Ai¡ Shipments by

Industry Group and Destination

Market Area

Study Team

Sourre TechniquedComments

Household Goods Carriers'

Highway Mileage Tables

Air Shipments by Industry and

Market Area

(Forecasts to 2010)

Colography Group

Census Foreign Trade Statistics

Study Team

North Carolina counties (NC), airport-based county groups for

parts of surrounding S-state region ("regional market areas" -

RMA), and "All Other U.S."

Dstances based on published highway mileage tables between

airports and county/RMA centroids.

BEA Employment Forecasts by

Industry and Region

Colography Group

Boeing World Air Cargo

Forecasts

Data source only includes top 70+ industries (at 4-digit SIC

level).

Expansion to all induskies based on Colography-supplied

expansion factors modified based on aggregate totals.

Exhibit I.2. Airport traffic characteristics.

ll¡te llam

NC traffic estimated from ratio of inbound to outbound for NC

airport traffic.

RMA traffic estimated as percentage of total non-NC traffic

based on outbound distribution.

Total US traffic estimated from Census statistics.

U.S. Airport Traffic (Baseline)

State Airport Traffic (Baseline) FAA Airport Activity Statistics

AOCI Worldwide'Traffic Report

Census Foreign Trade Statistics

(for Wilmington, NC, Customs

District)

Airline/Airport Interviews

t4t

Regional cargo shipment growth based on combination of

employment and cargo productivity growth by industry.

Employment growth rates derived from BEA proþtions

modified to match geographical and industry grouping.

Cargo productivity growth estimated for top industries using

trend analysis for Raleigh-Durham and Charlotte market areas

(from Colography); growth rates constrained based on national

aggregate proiections.

National totals projected using industry forecasts modified to

match time frame.

Colography Group

Census Foreierr Trade Statistics

Total NC Airport Traffic

(Forecast)

NC Primary Airports' Traffic

(Forecast)

TechniouedCommentg

Based on total U.S. Demand.

Market Demand Forecasts by

Market Region

Studv Team

International data only available by Customs District (in this

case includes all state airports).

Totai inbounci traííic is esiimaieci.

Total international outbound traffic includes re-allocation of

traffic enplaned on domestic flights at state airports for

transshipment at other U.S. intemational gateways.

Intemational inbound traffic assumes same expansion factors as

outbound traffic.

Domestic inbound traffic is estimated as the residual.

North Carolina Air Cargo

Forecasting and Allocation

Model

Airport proiections match market demand forecasts with

pro¡ected shrfts rn shaie (see below).

Model allocates total NC airport demand among primary

airports based on proximity to regional markets and relative

service levels.

Model incorporates assumptions about traffic diversion to

secondary airports.
142

U.S. Bureau of Census.T Some of the limitations encountered

and techniques used include:

. Comprehensive international data was only available in

the Census statistics for all airports in the V/ilmington,

North Carolina, Customs District which fortunately corresponded directly with the state's airports. International

estimates for individual airports required a matching of

carrier and aþort data with the aggregate totals.

. The FAA statistics exclude international carriers and

certain all-cargo operators, and only measure enplaned

cargo.

. The AOCI statistics incorporate filings by member airports which are not verified. Statistics provided by individual airports were used to verify this source for the

study.

. The definition of international cargo used in the ma¡ket

demand forecasts was based on the ultimate origin or

destination of the cargo. Airport traffic data may be based

on the originating flight (Census data) or type of air

bill (FAA). International traffic which included a transshipment at a U.S. airport was estimated and re-allocated.

. Where inbound data was limited, it was assumed that

patterns resembled outbound distributions.

Exhibit I.3 summarizes the estimated baseline traffic.

In addition to traffic patterns, a representation of relative

service patterns was required for the forecasting model. Service levels can be measured in terms of total cargo capacity

modiûed to reflect the timing and type of capacity. The study

team derived relative service levels forboth the domestic and

international markets based on the interview process and a

comparison of freight-flow patterns with air-service patterns.

These service indices were used in the allocation model and

modified during the calibration process. Future service levels were projected based on general scenarios related to

available capacity and carrier operating patterns.

Airport traffic flows were compared with market demand

to establish baseline cargo routing patterns which were then

projected and applied to future market demand (see below).

Forecasts of traffic for individual primary airports (including

the GACIC) utilized a calibrated route allocation model

described in the next section. Secondary airport traffic was

assumed to maintain the same share of the projected state

total throughout the forecast period.

Forecasts of aircraft activity at primary airports were

required for the capacity analysis, as well as to size the proposed GACIC facility. Exhibit I.4 summarizes the characteristics measured. The baseline distribution between passenger

and all-cargo flights for each airport was derived from the

modified FAA carrier statistics for enplaned cargo. Inbound

patterns were assumed to be the same. Average cargo loads

per flight operation were derived for both types of flights

with passenger patterns assumed to remain constant during

the forecast period. For all-cargo operations, average payloads were derived for general classes of aircraft type and

weighted based on current and projected flight schedules.

This structure was used to measure the impact of trends in

equipment technology and fleet mix. Exhibit I.5 shows how

aircraft operations were projected.

Cargo Routing Patterns

The interaction between market demand and facility activity can be represented by cargo routing patterns which

describe the facility's share of available markets. In this case,

the proximity/level-of-service procedure described in Chapter 4 was used to produce initial estimates of baseline cargo

routing patterns. The system was then calibrated to produce

better estimates of current market and facility activity, and

these results were projected for forecast years to derive traffic forecasts for each facility. The two-stage forecasting

methodology first assigned total flows to North Carolina airports and then allocated that traffic among the primary cargo

airports. Exhibit I.6 describes the characteristics used to

represent cargo flow patterns.

The state's share of available domestic and international

cargo markets was derived for each of the market demand

areas (North Carolina, regional market areas, and all other

U.S.). U.S. Census statistics matching state of export shipments with the airport of exit provided information on the

share of state airport traffic which originated in North Carolina. These results were modified to include international

shipments loaded on domestic flights for transshipment

at another U.S. airport (traffic which is not included in the

Census totals).

The State of Export series was also utilized in estimating

the share of state airport traffic originating from the regional

market areas. BEA data measuring personal income for these

regions was used to allocate state totals to the sub-state

regions. Domestic market distributions were based on the

interview program, as no routing data was available. Inbound

traffic distributions were based on the outbound patterns,

assuming each ma¡ket region accounted for comparable

shares oftraffic in both directions.

The market share forecasts were based on the study team's

analysis of historical trends and interviews with industry participants concerning future service development plans. Baseline shares were estimated for 2000 and 2010 and matched to

projected demand totals in order to forecast total traffic.

Exhibit L7 shows the structure used for these forecasts.

Forecasts for the primary cargo airports utilized a cargo

routing model which allocated the assigned state totals based

on a combination of proximity and service levels. The structure for the North Carolina Air Cargo Forecasting Model

(NCACFM) is shown as Exhibit I.8. The model's structure

includes the following components:

. The forecasting of state airport traffic from the Colography baseline data base is incorporated within the

model (shown as the top half of the exhibit). 7 U.S. Bureau of the Census, U.S. Exports/lmports of Merchandise, tape, annual.
Exhibit I.3. 1990 baseline North Carolina airport traffic (tons).

Ouöourrt

Domestic

International

Total

Inbound

Domestic

Inte¡national

Total

Total

Domestic

International

Total

CLT

55,995

10.121

6,7',t6

e,887

14.088

58,975

100,882

24.209

125,091

55-7Vo

33.0Vo

49.TVo

89.2Vo

GSO

10,857

74.2ß

25,105

5397

19.E33

25,230

't6,2V

34.081

50,335

9.OVo

46.SVo

79.ïVo

l00.SVo

Percent of Total

Domestic

International

Total

hùourd as a Percent

of Outbound

28,818

6.287

35,105

37,267

8.752

40,019

60,085

15.039

75;r24

33.ZVo

20.SVo

29.SVo

1,14.ÙVo

Subtotal

95,670

30.65ó

126,326

81"551

42.673

124,224

177,221,

73.329

250,550

97.8Eo

l0O.0Vo

98.LVo

98.3Vo

All Other Total

Aimorts

The primary airports a¡e defined by their distance to the

regional origin/destination (O/D) "zones" (North Carolina county or regional market area) and their relative

service levels for the domestic and international markets. The airport with the highest service level was

assigned a value of 100 percent with other airports' values set relative to that level. Forecast values were set relt.

2.

International inbound traffic estimatd as 739.2Vo of outbound traffic based on the ratio

of 1990 Wilmington import weight to reported international waybill exports.

Total inbound traffic estimated as percentage of outbound traffic.

2,005

__0

2,005

97,675

30.656

r28,337

83,522

42.673

126,195

't87,197

73.329

2ilþ26

10i].ÙVo

l0fl.07o

7û.ÙVo

98.3Vo

143

7,977

0

1,97r

3,976

0

3,976

Exhibit I.4. Airport aircraft activity characteristics.

2.2Vo

0.ÙVo

7.8?o

98.37o

Data Item

Primary North

Carolina Airports'

Aircraft Activity by

Type of Carrier and

Aircraft

ative to the baseline values based on anticipated service

development patterns.

. Cargo originating or terminating at an O/D zone (and

designated for a North Carolina airport) is assigned

among state airports using an equal weighting of the

relative service ratings and a distance comparison

weighted towards the closest airport. The service and

Primary North

Carolina Airports'

Average Load per

Flight Operations

Sou¡ce

FAA Àirport

Activity Statistics

Airline Interviews

Primary North

Carolina Airports'

Service Levels

FAA Airport

Activity Statistics

Airline/Airport

Interviews

Study Team

TeclrniouedCommmts

Assume inbound patterns

refl ect outbound patterns.

OAG Air Cargo

Guide

Study Team

Cargo flight payloads

estimabd for nominal

aircraft type.

Relative service indices

estimated for primary

airports.
Exhibit I.5. Summary forecast of aircraft operations at the North Carolina commercial airports.

Total Cargo Traffic (ü)0 Tons)

(-apo Activitw - Passe¡oer (}mati<rns

Allocation of Total Traffi c

Trafûc (000 Tons)

Numberr of Flight Operations (000)

F'nninna¡l Miv 

- 

All Caroo ()ncrafions

Percent of Total Qperations

Feeder

|et - Snall

|et-Medium

Jet - L"arge

Average Payload (Pounds/Operation)

Feeder

fet - Small

fet-Medium

fet - large

Weighted Average -Iet Op€rations

1990 199s

725.7 247.9

38.5% 38.5%

48.2 95.4

238.1 282.0

405 677

Charloüe

2000

424.3 7,779.9

Carqo Activitv - All Carqo Op€rations

Allocation of Total Traffic

Traffic (000 Tons)

Reder

Iet

Number of Flight Operations

Feeder

Iet

1.6.77o 79.0%

50.07" 28.6%

33.37o 42.9Vo

0.0% 9.57o

1,300 1,300

24,000 24,000

45,000 45,000

76,000 78,000

32,400 4't,477

38.5% 3E.5

763.4 454.3

927.0 449.7

1,018 2,023

1990

50.3 96.9 155.2 431.8

8.1,% 8.7Vo 8.1% 8.7Vo

4.1 7.6 12.6 35.0

238.7 282.0 327.0 449.7

Greensboro

1995

23.81o 14.3

74.37ø 4.8

42.9Vo 42.9

't9.070 3E.1

3,250 3,250

26,000 26,000

48,750 48,750

E4,500 E4,500

53,422 63,375

2000

27,217

4 56 78 156

67.íVo 6'1.57o

0.6 1.1

76.3 151.3

76.9 1.52.5

942 '1,777

4.7't7 7.299

5,654 9,016

54.5% 46.270 43.8% 47.4?0

36.47o 23.77o 72.5% 5.3Vo

9.lVo 75.4Vo 3'l.3Vo 26.3Vo

O.ïVo 75.4Vo 72.57o 27.7Vo

1,300 1,300 3,250 3,250

24,000 24,000 26,000 26,000

45,000 45,000 48,750 48,750

78,000 78,ooo E4,500 84,500

28,200 45,429 51,839 60,775

819 47,476

75.7

35.5%

26.7

238.7

224

Raletgh-Ilurham

67.5Vo 6'l

786

4.9 6.7

256.7 779.5

260.9 725.6

2,996 3,784

9.587 22.706

12,583 26,490

t35.7 228.8 628.7

1.3,527 25,062 30,469

35.5Vo 35.5Vo 35.5Vo

48.2 87.2 222.5

282.0 327.0 449.7

342 506 997

À

è

97.9% 91.9% 9'1..97o 9'1.9?o

2.4 2.'t 6.7 't8.2

43.8 86.9 136.0 378.6

46.2 89.1 742.6 396.8

3,728 3,280 4,096 77,273

3.707 3.827 5.266 72.459

6,384 7,707 9,362 23,672

26.77o 26.3Vo

60.OVo 47.47o

't3.3Vo 26.3?o

O.OVo 0.0Vo

1,300

24,000

45,000

78,000

27,878

25.980 25.9Vo

33.37o 29.6Vo

37.0Vo 37.0fo

3.77o 7.4Vo

1,300

24,000

45,000

78,000

31,500

20.747

3,250

26,000

48,750

E4,500

40,300

64.5% 64.57o 64.5% 64.5%

0.8 1.3 4.1 10.4

47.6 86.3 143.5 393.9

48.4 87.5 1,47.6 404.2

7,245 1,956 2,493 6,378

3.424 5.477 7,723 78.224

4,670 7,432 9,616 24,602

553

3,250

26,000

48,750

84,500

43,225

30 E61
Exhibit I.6. Cargo routing pattern characteristics.

Total NC Airports' Share of

Outbound Traffic by Market

Origin (Baseline)

Data ltem

Total NC Airports' Share of Study Team

Inbound Traffic by Market Origin

(Baseline)

Total NC Airports' Share of

Total Traffic by Market Origin

(Forecast)

Census State of Export and

Foreign Trade Statistics

BE^A,, Local Area Personal

Income

Study Team

Sou¡ce

Primary Airports' Share of State

Airport Traffic by Market Origin

(Baseline/ Forecast)

distance weighting factors were varied to calibrate

model results to actual baseline market shares.

. Projected cargo volumes are utilized by the airport

activity module which estimates flight operations.

The model's summary inputs and outputs are shown in

Exhibir I.9.

New Industrial Activity

The proposed GACIC facility would operate as a general

caron ¡irnort âtfrâctino resional caroo haserl on nroximitv

---^--____Þ 

_-Þ_-__-- '-- Í- "--'---'J

and service levels, as well as a magnet to new industrial facilities attracted by the integration of industrial and transportation capabilities. The forecasting model was designed to

accommodate a new "primary" airport which would compete

for regional cargo which includes new traffic assigned to the

county where the facility is located. The study included testing ofthree different locations for the facility with additional

testing conducted in later phases of the development process.

The projection of new activity attracted to or near the

GACIC facility was based on an extensive interview and

analysis process which identified the types of industries

which would best utilize the advantages of the facility and

then profiled the industrial and transportation characteristics

for those industries. The characteristics used to describe and

estimate the new activity is shown as Exhibit I.10.

The GACIC concept was developed based on trends

toward integration of production and distribution systems

and an increasing reliance on air cargo among the newest

Share of export traffic originating in NC estimated using

Census pattems for international flight enplanements

averaged with study team estimates for other types of

tra ffic.

Share of exports from RMAs estimated using five-state

Census totals with RMA portion based on counÇ-based

manufachrring earnings.

Share of domestic outbound traffic based on study team

interviews and industry pattems.

Study Team

North Carolina Air Cargo

Forecasting and Allocation

Model

Study Team

TechniquedComments

Distributions based on outbound patterns for both domestic

and international traffic.

Shifts in market shares of regional traffic based on historical

trends and assumed service development relative to

145

competing airports.

Model calibrated to baseline traffic.

Forecast market shares based on relative proximity to

markets and service levels.

Model accommodates "new" airport as represented by service

levels and location relative to market areas.

high technology industries. It was assumed that the priority

industries would include top air cargo producing industries

currently attracted to the Silicon Valley or currently prominent in North Carolina. The profile of industrial and transportation characteristics for each industry was based on

baseline year activity for those areas.

The number of new plants assigned to the GACIC area in

the forecast period was estimated from the current level of

activity for the prototype areas. For example, it was estimated that the GACIC could attract 32 electronic computer

facilities by the year 2000, equivalent to 15 percent of the

cunent concentration in Santa Clara County.s Projected traffic anrl emnlnr¡mcnf r¡¡erc naln¡rlafcrl r¡cins âr/erâoc size anrl

activity factors, as well as general assumptions concerning the balance and composition of activity. Exhibit I.11

summarizes the projected new industrial activity.

Conclusions

The methodologies applied in this study were designed to

project activity for both the existing state airport system and

a new facility concept with no available prototype. The techniques used incorporated a wide variety of data sources and

attempted to profile the relationships involved in air cargo

markets accurately. Typical problems encountered included

the synthesis of data with varying levels of detail and defins The prcjected number of plants was based on anticipated growth in the industries

over the forecast period and a reasonable share of "new" plant locations. Additional ¡esearch for the master plan included a more detailed analysis of the probable

development scenarios.
Exhibit I.7. Air cargo matrix forecast 2010.

North Ca¡olina

Doqrestic

International

TOTAL

Domestic % of Total

Toof.NlO/Ds

Resional Market Area

Domestic

International

TOTAL

Domestic Vo of.Toþ|

%ofAllO/Ds

AllOther U.S.

Donestic

International

TOTAL

Domestic Vo of.Tolal

Toof AJIO/È

Total U.S.

Domestic

lnternational

TOTAL

Domestic VoofTotal

A

616.4

325.4

947.8

65.570

83.íyo

w2.5

4.6

147.1

69.7To

13.07o

Norttr C-ârolina Atrpr

Outbound Total

BC

7209 1,5373

233.7 s59.1

954.6 1,896.4

75.5% 70.5%

83.8Vo 83.6Vo

119.9 ?22.4

32.0 76.6

151.9 299.0

78.9Vo 74.47o

73370 13.2%

9.0

30.5

39.5

Ð-.ïVo

3.SVo

727.9

400.4

t,12E.3

&.5%

72.759Vo

6'1..2&37o

68.572Vo

DEF

237.E U8.1 516.0

205.6 747.7 353.2

M3.4 425.8 rì69.2

53.67o 65.3Vo 59.4Vo

2.7Vo 2;l.Vo 2.1%

1,054.8 ',ß2.3 2,M7.2

331.4 314.8 ffi2

1386,.3 '1,47;1 2,733.4

76.1Vo 76.68o 76.4Vo

6.6Vo 6.77o 6.7Vo

10,539.7 't0,491.4 2't,ß'.t.7

8,508.7 7,854.7 16,%2.8

19,048.4 18,345.5 37,394.0

55.3Vo 57.2% 56.2Vo

9'1,.'1.7o 91.37o 97.2Vo

77,E78.8 77,755.5 23,634.3

9,021.6 8,340.6 '.17,%2.2

20,9t0.4 20,@6.0 44,996.5

56.8Vo 58.5Vo 57.6Vo

10.5 19.5

21.9 52.4

32.4 71.8

32.4V" 27.lVo

2.8Vo 3.2Vo

851.2 7,579.1

287.7 688.1

1,138.9 2,267.2

74?Vo 69.7Vo

9.627To

70.æt%

9.8æE

0.093vo

o3't970

O.192Vo

27.U1%

3E.777Vo

31.428%

6.263%

3.812Vo

5.24lTo

Total U.S. Airports

GHI

854.3 Wg.O 1,853.3

530.9 381.4 912.3

1,385.2 1,380.4 2,765.6

61.7Vo 72.4% 67.070

6.37o 6.5Vo 6.47o

't,157.3 

',1522 2,3ú.5

376.0 3ø'6.8 722.8

7,533.4 '1,499.0 3,ß2.4

75.5vo 76.97o 76.2Vo

7.OVo 7.7% 7.OVo

10,w.7 10501.9 27,0fi.6

8,539.2 7,876.0 't6A15.2

19,æ7.9 18,377.9 37A65.8

55.3Vo 57;1.% 56.27o

t36.6%o 86.5Vo 86.6%

72,ffi.7 72,ffi.7 25,213.4

9,U¿2.0 8,628,3 10,050.3

22,V28.8 27,235.0 43,263.7

57.2Vo 59.47o 58.3Vo

90.373%

8939%

90.740Vo

5

o\

99.9U7Vo

99.681Vo

99.$ßVo

13.737Vo

96.1æ%

94.759Vo
Exhibit I.8. North

Entplotgnrcnt Grouth

Proiætioraçto2ù10 ßEA)

BEACounty

GroupbyStrC I

Carolina âir cargo

NClRhlAAitCûgo

Muainrcdogqhy)

forecasting and allocation model.

CI:URDUAbøtto

PtnductivityflgETn)

U.S. AiTClrtgo

Prloduction(ú-ologlryhy)

147

U.S. AirClrgo

Fo¡¿cests

Totalby Carrier

1990IBWeight

r99G2ol0Total

WeicñtbvPNCCA

eirpot no:enity Weighb by Deftt€d Mile Ranç

Søwio Ce Dmiptitots

Airport S€n'ice Weights by PNCCA (D/f)

Ma¡tct tæalage Percenages fc Secondary NC and
Exhibit I.9. Base-case calibration-future service/leakage shifts.

1990

Domestic (inbotrnd+outbound)

Imports

Exports

International - Total

Total Traffic

Regional Inbound/Outbound Tr¡ffic Ratios

I,tgt.4

2,529.9

2,316.7

4,8ú.6

14,038.0

NC Counties

Regional Market Area

All Other U.S. O/D's

Air Cargo Traffic (ü10 Tons)

199s 2000

rt,7tt6.8

3,515.3

3,2&.s

6,779.8

18,5fÉ.6

Airport Proximity Profile

Mileage Range

75,187.1

4,8ß.9

4,557.3

9,396.2

24,583.2

0.8551

1.0045

N/A

0-50 Miles

51-100 Miles

101-200 Milers

Over 200 Miles

2010

1.3920

1.0842

N/A

25,213.4

9,1æ.9

8,881.4

18,050.3

43,263.7

Relative

Weisht

NC Secondary Airports Non-NC Airports

Percent of Total Traffic Leakage

1.56%

0.00%

0.00%

100

50

25

10

Average Annual Growth Rates

5.1Y"

6.8

7.7

6.9

5.8

Charlotte

Greensboro

Raleigh-Durham

Airport

0.00%

0.ffi%

O.t0"/o

Market Leakagee

5.2%

6.6

6.9

6.7

5.8

37.U%

95.370/"

99.91"/"

1990

1ffi"/"

lSYo

53%

Domeslic

00

1Er5

5.2Y"

6.6

6.9

6.7

5.8

58.72o/"

99.40%

99.68%

lOSo/"

20"/o

48"/o

Annual Diversion f¡om Non-NC

Airpo¡ts - For€cast Period

2000

l07o/o

24Y"

430/"

0.50%

O.25/"

2010

to7%

24Yo

43"/"

1990

77"/"

100%

36"/"

l.úY"

o.500/o

Intemational

77o/o

85Y"

44To

80o/o 80"/"

65% 65%

60"/" ûY"

2010
Exhibit I.9. (continued).

Origin/Destination

County/Area

Total by O/D Group

North Carolina Counties 7990

1995

2000

2010

Regional Market Areas 1990

1995

2000

201,0

All Other U.S. Counties 1990

1995

2000

2070

Total U.S. 7990

L995

2000

2010

Total Traf fic Generated

254.2 1.40.4

467.2 243.8

740.6 378.0

1,,853.3 972.3

328.8 130.4

612.8 220.3

978.6 335.7

2,,309.s 722.8

8,,608.4 4,575.7

70,706.8 6,315.7

13,.467.9 8,682.5

27,050.6 16,4't5.2

9,.797.4 4,846.6

l't,786.8 6,779.8

15,187.1 9,396.2

25,21.3.4 18,050.3

394.6 85.9

710.9 168.0

7,178.6 280.0

2,76s.6 755.7

459.3 9.8

833.1 23.3

1,314.3 45.6

3.032.4 148.1

13,784.2 4.8

17,022.5 6.0

22,1.50.4 7.7

37,465.8 12.0

14,038.0 100.5

1.8,566.6 't97.5

24,583.2 333.3

43,263.7 91,5.9

Charlotte

Int'l Total Dom Int'l

1990

1995

2000

2010

NC Prima¡ry Airport Totrl

Dom. Int'l

18.9 104.8

40.2 208.2

72.3 352.3

209.8 965.5

0.3 10.1

2.7 26.2

8.0 53.6

33.8 181 .9

5.0 9.8

7.5 73.6

10,8 18.5

20.4 32.5

242.2 '.124.7

50.5 2247.9

91.'t 424.3

264.0 7,779.9

177.3

340.7

568.1

1,550.2

Greensboro

14.3 27.0

35.0 44.8

68.7 59.0

184.0 '169.4

1.5 0.4

4.6 3.0

10.6 6.5

33.0 25.8

0.7 7.1,

1..2 8.3

1.7 8.8

2.7 16.6

16.5 34.4

40.8 s6.1

81 .0 7 4.3

21,9.7 212.7

73.3

139.8

240.3

688.1

Tot¡rl

Raleigh-Durham

Dom. Int'l Total

25,0.6

480.5

80r8.4

2,23,8.3

41..3 53.9 12.0 65.9

79.8 91.8 27.8 119.6

1,27.0 737 .1 62.6 199.7

353.6 368.6 179.7 548.3

1..9 3.8 0.1 3.9

7.6 7.9 1.1 9.0

17.1 13.6 4.3 77.9

58.9 47.2 17.0 58.2

7.8 2.5 2.5 s.l

9.5 2.8 4.2 7.1.

10.5 3.1 8.1 1,1.2

19.3 4.8 15.3 20.2

51.0 60.3 1.4.7 74.9

96.9 1.02.5 33.3 135.7

155.2 153.8 75.0 228.8

437.8 414.7 212.0 626.7

Charlotte

57Vo 33Vo 50Vo

587o 36Vo 52Vo

59Vo 38Vo 527o

59Vo 38Vo 537o

Int'l Total

Percent of NC Airport Total

G¡eensbom

Int'l

9Vo

l27o

l4Vo

74Vo

47Vo 20Vo 347o 20Vo 30Vo

407o 20Vo 30Vo 24Vo 287o

3l7o 79Vo 27Vo 37Vo 287o

31.Vo 797o 277o 37Vo 28Vo

Total Dom. Int'l Total

Raleigh-Durham

À\o
Exhibit I.10. New industrial activity characteristics.

Target Industries for GACIC

Data ltem

Numberof Plantsand Employment for

GACIC Industries

(2000/2010 Forecast)

Colography Group

Industry Inten¡iew Program

Source

Air Cargo Production (Tons and

Value) for GACIC Industries

Colography Group

Statistical Abstract of the United

States

FAA Airport Impact Study

Industry Interview Program

StudyTeam

Assumes GACIC would attract high technology companies dependent on

air cargo similar to Silicon Valley plus expansion of industries already

active in North Carolina.

New plants would be attracted in and around GACIC.

Selected top air-ca¡go producing industries (Colography industries)

from Silicon Valley (Santa Clara Co. CA) and North Carolina based on

plant size and air cargo productivity per employee in 1990.

14 from Silicon Valley and 8 from North Carolina (at fourdigit SIC

level).

Technioues/Comments

Projected number of plants assumes share of existing Silicon Valley (15

percent) and North Carolina (10 percent) locations in 200Q expanding

by 5 percent annually to 2010.

Proiected employees per plant in 2000 is assumed at 1990 levels with 1

percent annual growth to 2010.

Employment for industries supporting manufacturing activity estimated

based on U.S. ratio of transportation/public utility sectors to

manufacturing s€ctor.

Toùal employment impact on sÞte estimated using multipliers from FAA

study (Measuring the Regional Economic Significance of Airports,

October 1986).

Colography Group

StudyTeam

Growth in outbound cargo tons per employee for San Francisco market

area (at twodigit SIC level) from 1983 to 1990 extrapolated to 2000 and

2010 and applied to 1990 averages for Silicon Valley/North Carolina

industries.

Average value per pound assumed at 1990 levels using constant dollars.

Outbound cargo generated by industries supporting manufacturing

activity assumed at 50 percênt of manufacturing total.

Inbound traffic assumed equal to outbound volumes.

O
Exhibit I.11. Summary of air cargo produced by the GACIC and the

economic impacts of GACIC activities.

Cargo Impact (Tons)

New Traffic by Industry Type - Outbound

Manufacturing

Supporting Industries

Domestic Percent of Outbound Traffic

Ratio of Inbound-toÐutbound Traffic

New Traffic by Cargo Type - Summary

(a) Domestic

or¡tbourd

Inbourd

(b) International

Oulbound

Inbound

(c) Total

Oulbourd

Irìbourd

Employment Impact (Nurnber of Full-time ]obs)

Direct Employment

Manufacturing

Transportation Support Industries

Employment Multiplier

Total Employment

Revenue tnpact (Million $)

Direct Revenues

Manufacturing

Transportation Support Industries

Net State Impact (507o of Drect Impact)

Revenue Multiplier

Total Revenues

Forecast Year

40,9U

20.492

67476

45To

1.0000

27,6&

27.&

55,328

33,812

$412

67,624

67,476

6r.476

722,952

23,594

4.318

27,912

2.72

59,173

$3,411.2

172.7

$3,s83.9

97,79'1..9

2.72

93,798.9

739A39

69720

2@,159

4O7o

r.00m

83,63

83.663

167,327

72sA95

125.495

250,990

2W,759

2@.159

478317

40,w

7.ß7

47,756

2.12

101,242

$11f37.8

2955

$12,133.3

$6,066.6

2.72

$12,86,1.3

151

itions, identifying and defining appropriate market ranges, and

the incompatibility of historical data sets. The selected

approach was designed to allow flexible scenario testing and

assule that projected activify was based on a reasonable market allocation process as opposed to an independent trend projection of existing traffic.

1.2 CASE STUDY: TRUCK SIZE AND WEIGHT

POLICY

Truck size and weight regulation is an important policy

issue that demonstrates the use of many of the techniques

discussed in this chapter.

Any change in Federal or State size and weight regulations

can be expected to have at least some effect on the vehicles

used and the cost ofusing these vehicles, and usually on shipment sizes and vehicle-miles of travel (VMT) by various categories of trucks. These changes, in turn, can affect highway

safety, congestion, bridge and pavement costs, Tuei consumption, emissions, diversion of traffic between rail and

truck, overall costs for truck and rail transportation and for

associated logistical functions, and the financial viability of

affected truck and rail carriers. For most potential policy

changes the direction of the various effects is fairly obvious,

but their magnitudes are not.

Since all or nearly all potential policy changes produce a

combination of desirable and undesirable effects, careful

estimates of a relatively large set of likely effects of such

changes are required in order to evaluate the overall desirability of the changes. The development of these estimates

requires assessments of the likely effects on demand for and

use of affected truck configurations and competing rail service. The development of these estimates for a specified policy change of some interest is presented below. A thorough

analysis of the effects of this policy change would use these

assessments as the basis for developing additional estimates
t52

ofthe effects on (at least) safety, congestion, highway costs,

transport costs, and the viability ofcarriers.

1. Define Policy

The first step in the analytic process is the development of

a clear operational definition of the policy to be analyzed.

For the purpose of this case study, we consider the possible lifting of the 80,000 pound cap on gross vehicle weight

(GVW) from the Interstate System (IS), from all other rural

principal arterials, and from all other urban principal arterials that are on the National Network for trucks. Under this

policy, GVWs on this system of roads would be controlled

by the number and spacing of axles as specified by Bridge

Formula B. This formula would produce effective GVW limits of about 86,500 pounds for six-axle semi-trailer combinations (six-axle "semis") and about 108,000 pounds for

nine-axle twin 28-foot trailer configurations (twin 28s).

It is assumed that access provisions for trucks with GVWs

above 80,000 pounds to origins and destinations located off

the Principal Arterial System are fairly liberal, with the most

restrictive provisions in effect in several Mississippi valley

states. It is assumed that all existing length limits remain

unchanged.

This policy was one of several analyzed by Sydec in the

last several years in a series of studies of possible changes in

truck size and weight limits.e

2. Base-Case Forecasts

These sources were used to produce forecasts of VMT and

truck payload ton-miles for 1995 by trailer and axle configuration, region, GVW range, and highway system (distinguishing IS vs. non-IS and rural vs. urban).

3. Gost lmpacts

The third step in the analysis is to estimate the effect of the

policy on transport costs and, if necessary, on other logistics

costs.

For this purpose, a detailed analysis was conducted ofhow

the costs of linehaul operation of combination trucks vary

with trailer and axle conf,guration, trailer type, and GW/.

The analysis considered variations in costs for drivers, fuel,

equipment, tires, maintenance and repair, and overhead, as

well as the effect of empty movements on costs per loaded

mile. The resulting estimates of linehaul truck costs per

vehicle-mile and per ton-mile used in the original Sydec

study were presented in a working paperr2 and later revised

and updatedr3 for use in a subsequent study. Excerpts from

the updated working paper are presented in Appendix F.

The analysis also considered extra per-trip costs for

assembling and disassembling double-trailer confi gurations

(estimated in 1988 dollars to be $30 per trip for vans and $15

per trip for dump trailers), and for cleaning extra tank trailers (estimated to average $100 per trip for chemicals and $20

per trip for food products).

4. Changes in Vehicles Used

The fourth step consists of estimating how changes in the

cost of operating various truck configurations are likely to

affect usage of these configurations. These estimates were

developed by Sydec judgmentally by considering the advantages and disadvantages of switching to configurations that

can operate at the higher weight limits being considered.

Separate estimates of the extent to which carriers are likely

to change configurations were developed for carriers operating different trailer types. These estimates were based on the

Step 3 cost estimates, information obtained in a series of carrier interviews, and other information about the trucking

industry. Estimates of overall conversion to new configurations were then developed by combining the estimates for the

separate trailer types with data on the relative usage of the

different trailer types obtained from the Bureau of Census'

Truck Inventory and Use Survey.

The first two subsections below present a general discussion of: the opportunities for reducing truck transport

costs that would be created by the policy option analyzed;

and the varying effects that these opportunities would be

The second step in the analysis is to develop forecasts of

truck usage in the absence of any change in policy.'0 These

forecasts were developed using:

. Highway Performance Monitoring System (HPMS)

estimates of 1987 VMT by truck configuration, state,

and highway system;

. Truck Weight Study data on average overall GVV/ and

average GVW of empty trucks by truck configuration

and state; and

. Forecasts of VMT growth between 1987 and 1995 for

two categories of combination truck from the

FHWAÆaucett VMT Forecasting Model. I I

e This policy was one of seven studied in Sydec, Inc., Jack Faucett Associates, and

the Transportation Consulting Group, Lnc., Truck Size and Weight and. User Fee Policy Analysis Study, Part One, prepared for the Federal Highway Administration, March

1991. Additional policies were studied in: Sydec, Inc., T¡ansmode Consultants, Inc.,

and Jack Faucett Associates, Anal1,sis of Innger Combinatíon Vehicles, prepared for

the Federal Railroad Administration, Federal Highway Administration, and the Office

of the Secretary of Transportation, November 1993; Sydec, Inc., and Jack Faucett

Associates, Productiv¡ty Arulysis for Truck Weight Stady, prepared for the Transportation Research Boa¡d, October 1989; and Sydec, Inc., and Jack Faucett Associates,

Productivity and Consumer Benefts oflnnger CombinationVehicles, prepæed for the

Trucking Research Institute, Alexandria, VA, June I 990.

I0 Sydec, Inc., Jack Faucett Associates, and Ttansportation Consulting Group, Inc.,

op. cít., pp. II-10-I-13.

rl Herbert Weinblatt, Tlte FHWNFauceît VMT Forecasting Model,preparcd by Jack

Faucett Associates for the Federal Highway Administration, Washington, D.C., August

1988.

''? He¡bert Weinblalt, The Effect of Size and Weight Limits on Truck Costs,Workjtg

Paper, prepared by Jack Faucett Associates, fo¡ the Fede¡al Highway Administration,

June 1990.

t3 lbid., vpdated, October 1991.
Exhibit I.12. Relative efïiciency of alternative configurations for weight-limited truckload

linehaul operation.

6 Axle48 Foot

Semi

5 Axle Twin 28

9 Axle Twin 28

Loaded

Weight

0bs)

80000

86J00

80,000

91J00

80,000

108,000

1 Forty-two foot trailer for tanks and hoppers; 3ó feet for dunp trailers; 48 feet for other traller

types.

Source: Herbert Weinblatt, The Effect of Size anil Weight Littits on Truck CosÉs, Working Paper,

prepared by Jack Faucett Associates for the Federal Highway Administration, Washington,

D.C., Revised, October 1991.

Percent Cha¡rge in Cost per lon-Mile Relative to Five Axle Senil

Dry Van Reefer Flatbed Tmk Hopper lì¡rrp

expected to have on different types of trucking operation.

For each policy option, the information presented in these

two subsections was used to develop estimates of the overall effects on VMT and payload ton-miles transported by

vehicle configuration and region (and also the effects on

transport costs by region). The final subsection below

describes the way in which the information presented in the

first two subsections was used to develop estimates of the

impacts on configurations used.

Potential Savings

Exhibit I.12 lists several vehicle configurations that could

be operated at weights above 80,000 pounds under the policy being considered. For each configuration, this exhibit

shows the estimated percentage difference between linehaul

costs per ton-mile for typical truckload operation of that configuration when it is loaded to the indicated GVW and the

corresponding costs for a five-axle semi loaded to 78,000

pounds, a practical weight attainable under current weight

limits.r4 Percentage cost differences are shown for six trailer

types: dry vans, refrigerated vans ("reefers"), flatbeds,

tankers, hoppers, and dump trucks. The percentage cost differences were derived using empty mileage ratios that vary

by trailer type. However, for each trailer type, the same

empty mileage ratios and annual mileages were assumed for

all configurations, and all loaded miles were assumed to be

operating at the indicated GVW.

The estimates shown in Exhibit I.12 indicate that, except

for reefers and dump trucks, linehaul operation of nine-axle

twin 28s at Bridge Formula B limits is 11 to 15 percent less

expensive per ton-mile than operation of five-axle semis at

78,000 pounds. The smaller (9.5 percent) saving for reefers

+2.lVo +7.77o +2,27o +4.77o

-8.5 -9.3 -8.4 -6.1

+6.1 +16.6 NE -8.9 -3.0 NE

+35.5 +52.8

-15.3 -9.5

+37.4 +38.0 +41.5 -9.5 -13.0 -11.0

NÊ

NE

+4.27o

-6.4

NE

NE

153

NE

NE

NE

NE

+30.0Vo

-27.5

is due to the extra cost of refrigerating two small trailers.

The large (21.5 percent) saving for dump trailers occurs

because the comparison is made to semis with 36-foot trailers that are limited to a GVW of 70,000 pounds by Bridge

Formula B. (The length of dump trailers is limited by

stability considerations.)

Exhibit I.l2 also indicates that linehaul operation of sixaxle 48-foot semis at the Formula B limits is six to nine percent less expensive per ton-mile than operation of five-axle

semis, and that linehaul operation of five-axle twin 28s at

these limits is about nine percent less expensive for dry vans

and three percent less expensive for reefers. Five-axle twin

28s are included in Exhibit I.l2 and discussed in this Case

Study because the possibility of allowing these vehicles to

operate at Bridge Formula B limits was considered in the

study being summarized here. However, it since has been

concluded that such a possibility would be undesirable

because of the pavement damage caused by heavily loaded

single axles, and this possibility has not been considered in

any of the more recent Sydec analyses.

The estimated savings presume that there is no difference

in vehicle utilization between the five-axle semis and the

alternative configurations; i.e., there is no difference in

annual mileage, empty rnileage, or the extent to which loads

carried fall short of those that would be allowed under Bridge

FormulaB. Under the policy studied, however, many origins

and destinations cannot be served by vehicles operating at

GVWs over 80,000 pounds. To the extent that a vehicle

designed for operation at a higher weight would be used to

serve such an origin or destination, that vehicle would not be

utilized fully. As can be seen from Exhibit I.l2,itcosts more

to use such a vehicle for weight-limited hauls to or from such

shippers than to use a five-axle semi. The increased cost is

two to five percent for six-axle semis of various body types,

and is substantially higher for the heavier configurations.

The above discussion indicates that carriers of weightlimited cargo would be able to benefit from removal of the

IJ The percentage cost differences in Exhibit I. l2 are from the Octobe¡ 199 I version

of the refèrenced Working Paper rather than from the June 1990 version used in the

original Sydec study.
154

80,000 pound cap by switching from five-axle semis to various heavier configurations providedthey can do so with little or no reduction in effective utilization; i.e., there is little

or no reduction in annual loaded mileage and nearly all the

loads carried are ones that can be canied more efficiently in

the new vehicles that in frve-axle semis. Any reduction in

effective utilization reduces the savings obtained from the

switch in equipment, and it takes only a moderate reduction in utilization to negate the savings obtained. Hence, the

transport-cost savings that result from removal of the 80,000

pound GVW cap would be affected signifrcantly by the number of origins and destinations that can be accessed by

vehicles weighing more than 80,000 pounds.

Responses by Type of Operation

As the above discussion indicates, the ability of a carrier

to reduce transport costs per ton-mile by converting part or

all of its fleet from five-axle semis to an alternative configuration depends on its ability to use the alternative configuration primarily or exclusively to carry cargo at or close to the

Bridge Formula GVW limits. This ability depends on,the

cargo noûnally carried by the vehicle and the origins and

destinations served.

Consider operations that involve weightJimited carriage

between a relatively small number of locations. Such an

operation might be conducted by a private carrier between its

plants and warehouses, or by a contract carrier transporting

natural resources to any of several types ofcollection points

(processing plant, railhead, grain elevator, etc.). If all locations can be served by vehicles weighing more than 80,000

pounds with little or no increase in circuity, or if a portion of

the fleet can be dedicated to serving locations with this characteristic, a switch to vehicles designed for operation above

80,000 pounds would prove desirable. However, a change in

equipment generally would not be advantageous for operations of this type when any significant amount of service

is required to or from locations that cannot be accessed by

vehicles operating at Bridge Formula B limits.

The situation is somewhat more complex for operations

serving a relatively large number of locations. The most typical example of such an operation is a for-hire truckload carrier

that uses a single fleet of vehicles to serve a large (and frequently changing) number of shippers. For such operations,

replacement of five-axle semis by vehicles capable of operating at GWVs above 80,000 pounds would prove cost effective

only to the extent that the new vehicles could be used primarily to carry shipments that they can carry more efficiently than

can five-axle semis, and also to the extent that such shipments

can be obtained without any reduction in annual loaded miles.

This type ofoperator is likely to change vehicle configurations

only to the extent that it appears that the new vehicles can be

used primarily to carry such shipments. Because of mismatches between the characteristics of potential fronthaul and

backhaul traffic, it is likely that conversion to the heavier and

larger conflgurations would not be sufficient to carry all traffic that could potentially be carried by such vehicles.

To the extent that five-axle semis would be replaced by

vehicles designed for operation at GVWs above 80,000

pounds, use of the new configurations would vary by commodity and operational considerations. The remainder of this

subsection discusses the preferred confrgurations for several

different types of operation when the 80,000 pound GVW

limit is not a factor in vehicle selection.

For commodities carried in hoppers or dry-bulk tankers,

the preferred conflguration would nearly always be a nineaxle double, with six-axle 48-foot semis usually used where

access restrictions limit the use of twins but not the use of

86,500 pound semis.

For some liquid bulk commodities, the preferred conflguration frequently would also be a nine-axle twin 28. The use

of doubles for carrying chemicals and many food products,

however, would be limited both because the use of twin 28s

would increase tank-cleaning costs, and, in the case of chemicals, because many receivers want only a limited volume in

any one delivery. Accordingly, the preferred configuration

for chemicals and many food products would usually be a

six-axle 48-foot semi or a five-axle semi.

Because of concerns about stability, some reticence to use

twin 28-foot trailers exists among petroleum carriers. Thus,

the preferred configurations for carrying petroleum products

are likely to be six-axle 48-foot semis and three-axle trucks

pulling four-axle full trailers. Due to the high cost of tank

trailers, the phase-in period for new equipment would be

appreciably longer than for other trailer types.

Because of stability considerations, rear-dump trailers normally are no longer than 36 feet, and Bridge Formula B currently limits semis using these trailers to 70,000pounds GVW.

On a per ton-mile basis, Exhibit I.12 shows a 21.5 percent linehaul cost savings obtainable by switching to nine-axle twin 28s.

Twin dump trailers would have to be disassembled for

unloading (at an estimated cost of about $15 per trip), and, in

some cases, for loading as well. For this reason, semis and

single-unit trucks would generally still be preferred for hauls

of less than 25 miles and for some longer hauls as well.

Flatbed operators would be likely to prefer heavy doubles

for much of their operations where access restrictions do not

inhibit the use of twins, and to prefer six-axle semis for loads

that require longer trailers and also for access to locations

where use of twins is not feasible. Indeed, in the case of

flatbeds, a switch to six-axle semis is attractive even for vehicles that provide a significant portion of their service to or

from locations that can only be served by 80,000 pound rigs.

This is the case because the extra axle (or, alternatively, the

use of a spread tandem) usually is necessary in order to make

it practical to approach a GWV of 80,000 pounds while carrying loads that cannot easily be spread evenly across two

pairs of normally spaced tandem axles that cannot legally be

loaded above 34,000 pounds.

The benefits of higher weight limits to truckload operators

of dry vans are somewhat less significant. Most such vehicles

run cube-limited at least part of the time, and some private

carriers frequently carry only partial loads. Some carriers

focus on the cube-limited market with vans that are 53 feet

long or longer or with flve-axle twin 28s.
Because of thei¡ higher cost of operation, heavy twin 28s

would appear to be of interest to very few operators of dry

vans. However, both six-axle semis and five-axle twins

would offer some advantages for weight-limited carriage,

and five-axle twins would also offer advantages (relative to

48-foot vans) for most cube-limited hauls of over 200 miles.

Lifting the 80,000 pound cap on five-axle twins would enable

existing operators of twin vans to improve equipment utilization by competing effectively in both weightlimited and

cube-limited markets, particularly in several Eastern states

that sharply restrict the use of 53-foot trailers. Accordingly,

lifting the cap would be expected to result in a modest shift

from five-axle 48-foot vans to five-axle twins and a small

shift to six-axle semis.

The extra cost of refrigerating a second trailer limits the

attractiveness oftwin 28s for operators ofrefrigerated vans.

Accordingly, (except where longer combinations are allowed) the preferred new configuration would be six-axle

semis. The attractiveness of conversion, however, would be

limited by the likely difficulty of being able to utilize tully

the increased weight capacity offered by six-axle semisboth because of origins and destinations served only by

80,000 pound GW/ roads and because of the common use

of reefers for cube-limited cargo or partial loads on

backhauls.

Finally, raising GVW limits would have no effect on configurations used by LTL operators. However, replacing

existing weight limits on seven-axle triples by those allowed

under Bridge Formula B would permit increased loading of

triples in several Western states where constraints imposed

by 105,500 pound GVW limits would be eliminated. Similarly, eliminating the 80,000 pound cap on five-axle twins

would permit increased loading of twin 28s in a few LTL

traffic lanes in which a high proportion of dense cargo

results in some runs that are weight limited under the current GVW cap.

^,, -, +:A,;.- - +L - Dll--+n

vØØTLLLJ )/.t18 LrLc LJJcwLr

The effects of increased weight limits on VMT and payload ton-miles by conflguration and region were estimated by

interpreting the information presented above in the light of

specific characteristics of the policy options being analyzed.

Key information used in the analysis included:

. Region-specific characteristics ofeach policy option:

-The extent to which GVW limits would be raised; and

-The likely percentages of shipments originating or terminating at locations that could be efficiently served

by semis and doubles operating at the new GVW

Iimits.

. Likely responses of different types of truck operators (as

discussed above).

. Region-specific distributions of VMT across trailer

types obtained from a special tabulation of data from the

Truck Inventory and Use Survey (TIUS).

155

All impacts were estimated as changes from the 1995 base

case, but they represent the percentage changes that are

expected to occur at some future time when the nation's truck

fleet would have evolved sufficiently to allow fulI advantage

to be taken of the higher weight limits. The analysis assumed

there would be continued liberalization of access provisions

for twin 28s in Eastern states and no change in current access

provisions for 48-foot semis. Access restrictions were

assumed to reduce usage of twin 28s by 50 percent in New

England arrd 25 percent in the Middle and South Atlantic

regions relative to the usage that would occur in the absence

of these restrictions. The analysis assumed that the changes

in weight limits would not be accompanied by any changes

in size limits.

Specific substeps in estimating changes in vehicle usage

were:

1. Review information presented in the preceding subsections in the light ofregion-speciûc characteristics of

the policy option to determine the relative importance

of the potential responses of different types of truck

operation.

2. Apply these results to the TIUS data to obtain estimates

of the percentages of payload ton-miles currently carried by vehicle configurations (e.9., five-axle semis)

that would likely be diverted to various alternative

("new") confi gurations.

3. Estimate the distribution of divefed ton-miles across

operating weights when carried by the current vehicle

configuration and the corresponding distributions when

carried by the new configurations.

4. For nondiverted traffic carried by configurations for

which GVW limits would be increased (e.g., in

most states, frve-axle twins), estimate the shift in the

distribution of ton-miles across operating weights.

5. Obtain estimates of the total and percentage change in

payload ton-miles and VMT by vehicle conflguration.15

5. Modal Diversion

The final step in estimating the effects of changes in

truck size and weight regulations on transport demand is the

estimation of diversion to or from other modes.16 For this

purpose, the Sydec study used the results of a special run of

the Intermodal Competition Model (ICM), a proprietary

ì5 As an example of Steps 2, 3, and 5, the analysis suggested that, in the Middle

Atlantic region, 23 percent of traffic cutrently caried in five-axle semis would be

diverted to six-axle semis. (The percentage diverted varies by region due to differences

in the distribution of body types used and in curent GV'W limits.) Total ton-miles

diverled is 24.9 billion (23 percent of an estimated 108.2 billion base-case ton-miles).

The average payload of diverted traffic was estimated to be 16.4 tons when cmied in

five-axle semis (allowing for empty backhauls, partial loads, etc.) and 17.8 tons when

canied in six-axle semis. Accordingly, VMT of frve-axle semis was estimated to

decline by 1.52 billion (16 percent ofestimated base-case VMT), and, after adjusting

for a small increase in circuity, VMT of six-axle semis was estimated to increase by

l-41 billion.

tó An additional step estimating induced or suppressed demand can be performed.

However, because this effect is quite small and difficult to quantify, it usually is not

considered to be worth estimating.
156

model developed by the Association of American Railroads

(AAR). This model is described briefly in Section H.1, and

some results produced by this model are discussed in Section G.1.

For this run of the ICM, the Sydec study team provided

AAR with cost specifications that were designed to produce percentage changes in average truck payloads and in

truck costs per ton-mile relative to an existing base-case

ICM run that are identical to those developed in the Step 3

analysis of truck costs. Separate estimates were provided

for two alternatives to a conventional five-axle semi for

each of five trailer types (van, reefer, flatbed, tanker, and

hopper). For each trailer type, the alternatives consisted of

a six-axle semi and an appropriate double-bottom configuration.

The cost changes used were those for the most likely

alternative configurations for each trailer type. The results

of this run provided an estimate of the modal diversion

that would result if six-axle semis and twin 28s could be

operated at Bridge Formula B limits between every O/D

pair currently served by rail. The results of this model run

were then scaled to reflect the study team's estimate of the

actual ability of the alternative configurations to operate

between relevant O/D pairs at the proposed bridge formula

limits.

The results of each of the ICM runs included estimates, by

commodity group, of:

. Rail ton-miles diverted to truck;

. The corresponding increase in truck ton-miles (lower

than the rail ton-mile figures because of less circuity);

. Current railroad revenue from diverted traffic;

. Avoidable railroad costs associated with this traffic;

. Costs of truck transport for diverted traffic; and

. Reductions in railroad revenue resulting from competitive

rate reductions on other traffic retained by the railroads.

Each ICM run also produced a single estimate (not by

commodity group) of the relative usage of semis and twins

by diverted traffic. For each policy option, this last estimate

was used to split the scaled ICM estimates of increased truck

ton-miles between semis and twins. The increase in semitrailer ton-miles was then distributed across weight brackets

for six-axle semis using the same distribution as was used for

freight carried by five-axle semis converting to this configuration; and the increase in twin-trailer ton-miles was similarly distributed across heavy twin configurations and weight

brackets. The distributions of increased ton-miles across

weight brackets were then used to derive estimates of

increased VMT.
APPENDIX J

INFORMATION NEEDS

Identification of the information needs perceived by public agencies was an important objective of Phase I of this

study. Interviews with and surveys of public officials were

conducted to: (1) identify the freight demand questions they

would most like to see addressed in our research; (2) learn

about the methods they have used to address these issues in

the past; and (3) obtain any information about models and

data sources they may be able to provide to us. Specific

activities that were carried out included the following:

. Interviews with federal planners and policy analysts in

DOT and other federal agencies;

. A survey of state DOTs and other state agencies potentially interested in freight forecasts, with followup

phone calls to selected survey respondents;

. A survey of metropolitan planning organizations

(MPOs);

. A survey ofcoastal ports;

. A survey ofinland river ports; and

. A survey ofairports.

PERCEIVED BY PUBLIC AGENCIES

This appendix summarizes our findings from these surveys and interviews. Detailed survey results are presented in

three appendices to our Phase I report,r and a list of federal

interviewees is contained in a fourth appendix to that report.

J.l FEDERAL POLICY ANALYSTS

lyzing the future effects of potential policy changes. They

use exogenous economic forecasts produced by Data

Resources, Inc., and other private and public-sector economic forecasters for this purpose, but no more than passing

mention was made of these forecasts in any of the interviews.

Of substantially greater interest to federal policy analysts

is information that would provide a better understanding of

the freight demand system and the influences on this system.

Most of these persons expressed a strong interest in the

effects on transportation flows of changing patterns in international trade, and, in particular, in changes likely to result

from the North American Free Trade Agreement (NAFTA).

The interest in NAFTA is due not only to the immediacy of

the liberalization of trade rules and cross-border transportation operations, but also to the current high rates of growth

in the affected trade (12 percent per year with Canada, 7

percent with Mexico).

One person generalized the interest in international trade

to include all influences on transportation demand that originate outside of the transportation system, broadly defined. In

addition to international trade, such influences would include

changes in production processes, distribution systems, inventory ratios, commodity characteristics, industry location, and

demographics that might affect commodity flows, service

requirements, or modal choice.

A related set of interests centered around obtaining better

information about current freight demand. One broad component of this information would consist of commodity specific characteristics that influence the choice of modes and

vehicles to be used. These characteristics include density,

value, packaging, shipment sizes, lengths ofhaul (by region),

and relevant shipper/consignee characteristics. It was observed that commonly used data on commodity density were

developed many years ago and do not reflect the effects of

increased use of low-density packing materials.

Other desired freight-demand information includes better

data on truck flows, numbers of trucks and truck drivers,

truck VMT by time of day, distinctions between domestic

and international air freight and between the land and air portions of intermodal movements, the true origins and destinations of rail intermodal movements, and the true origins and

destinations of international movements (not exporter and

importer addresses). A federal truck-stop survey [along the

lines of the one conducted for the Association of American

Railroads' North American Trucking Survey (NATS)I was

suggested as one way of obtaining better data on intercity

r57

Our interviews with federal officials indicated substantial

differences between the needs of policy analysts and planners, so we prepared separate summaries for these two

groups.

Vy'ith some exceptions, the policy analysts we interviewed

had relatively little interest in forecasting as such. Some

interest was expressed in identifying likely improvements to

the intermodal system and the resulting effects on demand

for intermodal transport. One person was interested in

"strategic" forecasts that could identify transportation flows

that will grow significantly in the long run. Another expressed an interest in multimodal forecasts that are consistent

across modes, and a third in forecasts of the decline in lessthan-truckload (LTL) traffic. However, the primary use of

forecasts by these persons is to provide a platform for anaI Cambridge Systematics, Inc., et al., Characterist¡cs and Clunges in Freíght Trartsportation Demand, Intet\m Report, prepared for the National Cooperative Highway

Research Program, Project 8-30, Washington, D.C., August 1993.
158

movements of truckload freight. It was observed that the

Commodity Flow Survey (CFS) would likely be a good

source of data on nationwide traffic flows and modal shares

(of increasing interest because ofISTEA), but that disclosure

limitations would create major gaps for state-level analyses

and the data would be of little value for site-specific analyses. Nonetheless, one federal official thought a package to

translate CFS data for use by MPOs could be useful. An

interest was also expressed in obtaining data on rail and

motor-carrier rates.

Several federal policy analysts suggested our study

should assist states and MPOs in preparing the freight portion of Intermodal System Plans. One individual suggested

that we produce a "tool box" containing different tools for

large MPOs and for small MPOs. It was observed that, while

the former may have significant modal interface problems

to be addressed, the latter are more likely to think of

intermodal planning in economic development terms

(though not necessarily with any understanding of the

impact on development).

There was also clear interest in developing a set ofperformance measures for the transportation system, though this

would seem to be more a supply issue than a demand issue.

Policy issues of interest to federal officials include the

effects of NAFTA; truck size and weight policy (and the relationship between NAFTA and size and weight policy); deregulation and reregulation; jusfin-time requirements; strikes ;

ISTEA regulations; landside access to ports and airports; rail

mergers; rail abandonments; and weight-enforcement

problems related to truck transport of shipping containers.

J.2 FEDERAL TRANSPORTATION PLANNERS

Unlike policy analysts, transportation planners are specifically interested in transportation forecasts, quite frequently for

far into the future. Usually the forecasts of interest represent

the demand for a specific facility or set of related facilities.

The most active federal ffansportation planning agency is

the U.S. Army Corps of Engineers (COE). The COE and its

consultants have produced numerous forecasts of traffic on

different waterways using aggregate and disaggregate economic forecasts from various sources and raiVwater modal

split models. The COE is required to analyze the effects of

high and low economic forecasts, but ultimately all decisions

have been based on their "most likely" forecasts. These have

usually tumed out to overestimate waterway traffic volumes,

apparently in part because the costs ofunderdesigning waterway capacity are much higher than the costs of overdesigning, and in part because some of the COE's local information

sources perceive overestimates to be in their selfinterest. The

COE has conflicting goals of improving the uniformity of

their forecasts and of the methodologies used while allowing

methodologies used for specific analyses to be tailored to the

special characteristics of the situation.

Although many of the COE's past forecasts have been produced as part of the planning process for major waterway

projects, few such projects are anticipated in the fufure. The

current focus is on fine-tuning, fixing and maintaining the

waterway system, with a need for short-term forecasts using

the most current data obtainable. Short-term analyses are also

used for forecasting trust fund revenue. The new emphasis on

short term forecasting has resulted in an effort to base forecasts on monthly lockage data rather than on the more comprehensive and less timely waterborne commerce data. Environmental issues of increasing interest relate to analysis of

sediment flows, disposal sites for dredged material, where

fleeting should be allowed, and the type, volume and routes

of hazardous materials movements.

Other federal respondents mentioned state and MPO needs

for forecasts of access requirements at ports and rail facilities, and needs for port expansion. The U.S. Maritime

Administration has used forecasting in the past to estimate

port needs, but their only current use is to forecast the share

of ocean commerce carried by U.S. flag carriers to justify

federal subsidy and cargo-performance programs.

J.3 STATE AGENCIES

Surveys were mailed to the chief administrative officer of

all state departments oftransportation, asking for their assistance in identifying freight demand information needs and

current methods and data sources. Responses were received

from 38 states, a response rate of 76 percent. A copy of the

survey form, tabulations ofresponses to closed-ended questions, and summaries of responses to open-ended questions

are presented in Appendix C of our first Interim Report.2 That

appendix also contains summaries of follow-up telephone

interviews with DOT personnel in selected states.

More than half of all states surveyed indicated that they

had major planning responsibilities for truck, rail, and air

freight (see Exhibit J.1). Few indicated major planning

responsibility for ports and none indicated major planning

responsibility for pipeline or warehousing. Also, many states

indicated that they had regulatory responsibility for trucks,

rail, and air freight (see Exhibit J.2) and a few states indicated

that they had operating responsibilities (see Exhibit J.3).

States were asked to identify the planning and policy

issues for which freight transportation forecasts would be

most valuable to their agency. The most commonly selected

items by the 38 states responding were as follows:

. Highway needs analysis (36 states)

. Truck routes and restrictions (35 states)

. Highway planning (35 states)

. Truck size and weight regulations (34 states)

. Planning of trucVrail intermodal facilities (35 states)

. Airportplanning (31 states)

2 lbid.
Exhibit J.1. State DOTs with major planning responsibility for freight

(percent).

. Rail facility and access planning (31 states)

. Promotion of economic development (30 states)

Most of the states surveyed indicated that they had little or

no experience in freight forecasting. Exceptions were Iowa

(which indicated extensive use of traffic and commodity

forecasts to support efforts to identify needed transportation

improvements), Oregon (which used freight forecasts in

developing its statewide transportation plan), and V/ashington (which uses forecasts in rail abandonment cases and in

preparing its freight rail plan and airport system plan).

In response to a question about what sources of freight

transportation data do they use or currently have available,

common responses were as follows:

. Rail carload waybill sample

. Corps of Engineers data on waterborne commerce

. Truck counts and truck weight data

Nl Truck

E Rail 'ffi Air

EH Inland Water

@ Maritime (ports)

W Pipeline

E Warehousing

159

80

. Air cargo activity by airport

. Reebie and Associates Transearch data

. Census Bureau's Truck Inventory and Use Survey

J.4 METROPOLITAN PLANNING

ORGANIZATIONS

100

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(percent).

A total of 64 surveys were mailed to MPOs serving areas

with a population of 500,000 or more. A total of 33 completed forms from 24 different states were received, for a

response rate of 52 percent.

Vy'hen asked to indicate the level of importance which

freight transportation demand has with respect to the MPO's

responsibility for regional transportation planning and policy

issues, the greatest share view it as "somewhat important" to

"very important", particularly with respect to noise/congestion and traffic management issues and landside access and

facility planning (see Exhibit J.4).

Nl Truck

E Rait

ffi Air

ffi Inland Water

@l Maritime (ports)

ffi Pipeline

El Warehousing
160

Exhibit J.3. State DOTs with operating responsibility for freight

(percent).

The principal sources offreight data compiled or used by

individual MPOs include traffic and truck counts, some of

which are provided by state DOTs. Those MPOs which routinely use freight data rely primarily on data from secondary

sources or on data collected by DOTs, state trucking associations, airport or port authorities, and carriers. A few of the

MPOs have conducted freight surveys, albeit on an infrequent basis (every five to ten years). Only two indicated

they use commercial data services, specifically Reebie

Associates' Transearch database.

Of the 14 MPOs which develop or utilize economic and/or

freight forecasts, most indicate they currently focus on economic, demographic, and employment forecasts. Only three

currently use freight-related forecasts, two ofwhich focus on

truck and one on air cargo. Two MPOs anticipate increased

use of forecasts in the future.

Nineteen ofthe respondents (58 percent) currently include

freight-related facilities and issues in their transportation

improvement plans (TIPs), with particular emphasis on:

. Roadway improvements for enhanced goods movement

. Port and airport landside access

. Intermodal terminal access and development.

20

N Truck

I Rail

ffi Air

m Inland Water

@l Maritime (polts)

M Pþeline

l= warenouslng

40 60 80

The majority of respondents (85 percent) currently coordinate or integrate their freight transportation and facility

process with other public agencies; specifically:

r00

ISTEA provisions which expand the role and responsibilities of MPOs in freight and intermodal planning have

resulted in an increased involvement offreight industry interests in the MPO planning process. This has included: (a)

adding freight industry representatives to existing planning

and technical committees; and (b) organizing freight advisory councils or task forces which include local freight

interests.

Many of the respondent MPOs are seeking ways to

improve their knowledge and expertise in freight issues

either through additional staff, consultant support, or staff

training programs. Some expressed a need for better data and

tools relating to freight transportation.

Coordinate with

Stare DOTS

Port/Airport/Transit Authorities

Countyllocal Agencies

Carriers/Carrier Associations

Federal Agencies

Most MPOs anticipate increased coordination and

cooperation as a result of the ISTEA requirements.

J.5 COASTAL PORTS

A total of 53 surveys were mailed to major coastal ports

throughout the U.S. A total of 26 completed forms from ports

in I 3 states were received, for a response rate of 49 percent.

The breakout ofresponses by coastal region was as follows:

West Coast 8 Ports

South Atlantic 7 Ports

Gulf Coast 6 Ports

Great Lakes 3 Ports

North Atlantic 2 Ports

The vast majority of respondent ports indicated that freight

demand forecasting is an important component in their planning and operations activity (see Exhibit J.5). Approximately

two-thirds of the respondents also stated it was somewhat to

very important in relation to policy issues.

More than half (58 percent) of the respondent ports

develop their own freight forecasts, using methods ranging

from "best guess" and port user contacts to time series analysis and econometric modeling. They utilize both in-house

data gathered from carriers and shippers and commercial

databases such as PIERS and DRI-McGraw-Hil1.

No. of Respondents

23

20

14

8

1
Exhibit J.4. Importance of freight demand to port operationsþlanning functions.

Ø

o

Ø

c

o

CICN

o

E

o

ci z

Operations/Planning Function

o\
Exhibit J.5. Importance of freight demand to MPO policy/planning functions.

cn

o

Ø

c

o

oU)

o) tr

o

c; z

Planning/Policy Function

o\

N)
The principal sources of freight and freighrrelated data

compiled and/or used by the ports include:

Journal of Commerce PIERS 16

In-House Carrier/Shipper Data 14

Bureau ofthe Census 8

Corps ofEngineers 4

Trade Publications 2

Other Ports 2

U.S. Customs Service I

Statistics Canada 1

Maritime Administration 1

Other 3

Eleven of the respondent ports (42 percent) utilize commercial forecasting services, with DRI-McGraw-Hill's World

Sea Trade Service and Journal of Commerce Trade Horizons

being the two most frequently mentioned. Others included the

\ryEFA Group, BST Associates, and TRADE (formerly TIPS).

The frequency with which the ports develop or contract for

freight forecasts ranged from quarterly or semi-annually (on

a commercial contract basis) to every five years.

The majority of the respondent ports (65 percent) coordinate their freight transportation and facility planning process

with other public agencies and the private sector; specifically:

163

One respondent indicated planning is coordinated with the

local MPO, while another coordinates planning with state

and county agencies and private terminal operators.

J.7 AIRPORTS

Coordinate with

State DOT

MPO

City/County Agencies

Highway Transportation Authorities

Advisory Councils

Corps of Engineers

Railway Commission

State Dept. of Commerce

Carriers/Shippers

J.6INLAND RIVER PORTS

While a total of 30 surveys were mailed to those airpons

with the grcatest volume of air freight traffic, only seven airports responded, six with completed forms and one by letter.

The low response rate was not unexpected since the majority

of airpons focus far greater attention on passenger operations

than freight, and in many cases they have no staff positions or

personnel assigned to air freight issues or cargo development.

In recent years, several airports have begun to realize that

air freight services are or should be an important component

of their operations. However, planning for air freight operations remains farbehind that forpassenger operations. A case

in point is the new international airport at Denver. Air cargo

interests were so dissatisfied with the location proposed for

them at the new facility that several considered moving their

operations to an alternative site at Front Range, which was

being promoted as a potential all-cargo airport. Subsequently, plans at the new international ai¡port were revised

to ensure that air freight operations were more accessible to

the surface transportation network.

The concept of all-cargo airports has also grown in recent

years, with Alliance Airport in Texas being the most notable

example. Plans for a "global transpark"-intermodal and

industrial complex centered around an airport-has also

been proposed in the State of North Carolina.

Depending on the location of an airport, the issue of nighttime operations and associated aircraft noise is one with

which many airports continue to grapple. Because a significant share of air freight operations-particularly those of the

all-cargo and air express carriers-occurs at night using

older and noisier aircraft, this has been a matter of concern

and debate in many communities.

Of those airports that did respond to the survey, most felt

that freight demand forecasting was important to very important with respect to air carrier and all-cargo operations, as

well as land use planning and landside access.

Only two of the airports indicated they develop their own

freight forecasts. One indicated use of trendline analysis and

the other regression analysis to develop the forecasts.

For five of the six airports, the monthly airline reports of

pounds enplaned and deplaned a¡e the principal source of

freight data. Individual airports also indicated they use U.S.

DOT and Census trade data. Only one airport uses a commercial forecasting service, while another relies on an

outside consultant to develop periodic air freight forecasts.

Three of the respondents indicated they coordinate planning with federal, state, and local government agencies. One

coordinates with a private developer, while another has just

begun coordinating with the local MPO.

A total of 32 survey forms were sent to members of the

Inla-nd Rjver Ports a-nd Termina-ls Associa-tion; however,

responses were received from only four. Since the sample

was primarily public port agencies or commissions and most

inland river operations are privately owned and operated, the

low response was not unexpected.

Those that did respond indicated that freight transportation demand forecasting was most important with respect to

planning activities-land use planning and landside access. None of the respondents develops any type of freight

forecast, but rather periodically consults with terminal

operators and users.

The principal sources of data used by the agency respondents include the Corps of Engineers waterborne commerce

statistics, the USDA grain transportation statistics, and the

Journal of Commerce data.

No. of Respondents

l3

10

6

2

2