### 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. Bureauof Labor Statistics (BLS),



## ffiH,ååjiË:^T,{ilåiä.1',åË*tp", 

## $1224 $13

# 48r 1145288' 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 PPIfor 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 (sEt49). 

## Overhead U.fì. Bureau of Economic Analysis, Imolicit GDP deflator (currentdollar 

## ëifËäfu"däüiåì,äàääãiiåiäöb. 

# 1.03e 

# 7.261, 1'254 21vo 0'263 

# M

Overall Adjustment

## Factors 1.156 

## I Seetext. '? 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 toagricultural trucking 

### (#I),LTL general

### freight 

### (#3lI), andtruckload general freight (#312).



### Software Packages
This subsection discusses software packagesfor performing detailed analyses oftruck costs. Packages from two vendors are discussed below.2 The 

### first package, 

### MicroTOCS (produced 

### by Snavely, 

### King 

### & Associates

### of Washington, D.C.) is

### primarily designedto provide detailed cost estimates 

### for individual truckload (and 

### multi-truckload) shipments. The second package, theTruckload Cost Information System 

### (TLICIS) (produced by theTransportation Consulting Group 

### of Bethesda, 

### Maryland), has a more carrier-oriented focus, producing less cost detail for individual shipments but asub

### stantial 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; andthe number 

### of work stations and sites involved. TheTransportation 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 costsoftruckload movements. In MicroTOCS, the basic costing unit is atruckload shipment 

### from aparticular origin to aparticular 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 andthe cost of any asso

### ciated

### waitingtime. However, 

### empty return ratios are reflected 

### in the cost estimates. 

### Linehaul operating costs are estimated asconsisting of: 

### driver costs;

### fuel costs; miscellaneous costs;

### tires; maintenance; user taxes; and administrative and overhead. The estimates 

### oftire and maintenance costs distinguish thetractor and

### trailer components; and insurance costs and licensing andpermit charges are also distinguished. Total costs include 

### all operating costs plus capital costsfor 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; andestimated empty 

### return mileage. The remaining menus 

### providedefault 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 

### bytruckload carriers

### for analyzing the costs

### of various aspects 

### of their operations. 

### Accordingly, TLICIS provides lessdetail 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 anddelivery activities versus those paid in linehaulactivities. Accordingly, the

### TLICIS model requires carriers to break down 

### allof their costs 

### into individual components and then account for the cost components 

### by activity. The 

### majoractivity categories 

### for a

### truckload carrier are linehaul, loading, unloading, stopoff, claims, and 

### billing andcollecting. 

### At the most disaggregate level, the 

### progam produces output interms of costsfor aparticular 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 anystopoff 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 perton, 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 aspecific

### traffic lane,

### from a particular customer, or by aparticular salesperson, during any given time period. These summary reportsform 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 andtrips by means of an on-screen 

### "input 1og" which the user completes to describe aparticular 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 2Some 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 

### perton-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 1992was 3.03 cents perton

### mile. The average rate

## in 1994 can be estimated 

### by multiplying 

### by 1.017 

### (from the U.S. Bureau

### of Labor Statistics, PPIfor railroad line haul operations) to produce a rateof 3.08 cents per ton-mile. Extrapolating anadditionalyear 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

### tovary 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 actualtransport costs, estimates can beobtained 

### 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

### inthis 

### exhibit are all between 

### two andfour cents per

### ton-milewith
one

### very significant exception: the average rate 

### for transportation equipment is9.01 cents perton-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 fre

### quently shipped

## by unit train, qualifying 

## for significant volume discounts. 

### All Commoditíes

Source: Interstate C-omrrrcrte C-ommissiory 7992Caiload 

### 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 (ofWashington, 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 anyconsulting 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 eachshipment, 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-' uesto 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 andoperating 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 onshipment 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 

### individual124



### railroads and analyzed 

### throughthe URCS. 

## If the user 

### of MicroURCS purchases costing data 

### from 

### all the 

### individual railroads, then cost estimates 

### for agiven movement

### will be based on data 

### from theactual railroads participating 

### in the movement. Otherwise, the cost estimates 

### will be derived 

### from datafrom all the railroads operating in the appropriate region. The user begins the input process 

### by specifying informa

### tion 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 onthe equipment used, routing, and handling requirements 

### for the shipment. 

### If the shipment is aTOFC/COFC shipment, the user is asked whether the 

### railroad

### or the shipper 

### is supplying the trailer/container andthe number of units involved. The program requests 

### information on the number 

### of cars, car type 

### (with default cartypes selected onthe basis 

### of commodity specification), andwhether the cars are supplied 

### by the 

### railroador 

### by the shipper. The program also requests information onthe specific railroads in the route, the mileage on each railroad, the

### origin and destination, and any special handling required atthe 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 acosting section based on the 

### line-haul cha¡acteristics

### of theshipment. 

### This involves determination 

### of whetherthe specific shipment 

### is part 

### of a multi-car shipment. Switching costs atorigin and destination are reduced 

### by 75 percent 

### for unit-train movements and by 50 gram computes car-miles, gross tons, grosston-miles, 

### number

## of intra-/inter-train switches,

### and interchange switches. These 

### inputs and intermediate 

### results are used to develop detailed cost datafor 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 netton-mile.

WATER TRANSPORTATION 

### Typesof Operation

The structurefor the cost analysis depends on the operating and market pattems specifictova¡ious vessel and service types. The various types ofwater transportation can be categorized:



## . inland barge;



## . intercoastal tug-barge;



## . deep-sea 

### bulk carriers 

### (liquid, dry, and mixed);



## . deep-sea breakbulk carriers 

### (liner andtramp); and



## . deep-sea container carriers (liner).

Inland barge and intercoastal tug-barge operations gener

### ally are 

### limited to the carriage

### of bulk commodities. Barge transport 

### includesthe dedicated transport 

### of single commodities between two points, as

### well ascommon carrier dis

### tribution 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 andchemical barges atva¡ious points on the upper 

### river

### for transfer 

### to operators 

### onthe lower Mississippi at an interchange

### point. On the other hand, dedicated services

### will shuttle between a 

### limited number 

### of river points, 

### with the towboat oftenwaiting 

### 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 singlevoyages moving oneor more commodities, often on asingle charter basis. 

### Bulk and highvolume 

### or seasonal breakbulk commodities 

### typically move 

## in this fashion. 

### Trampoperationsgenerally include 

### full shipload lots and empty deadhaul legs between discharge and 

### load ports. Somevessels 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 aredesigned

### for containerized and general 

### breakbulk cargoes, and

### typically make 

### multiple port calls over a coastal range. 

### For example, a 

### NorthAtlantic carrier 

### might operate 

### four vessels

### with weekly calls at Charleston, 

### Baltimore and

### NewYork in the 

### U.S. and 

### Felixstowe and Rotterdam in Europe. 

### A single voyage for each vessel would take28 days. Container operators also maintain inland dis

### tribution systems 

### for their containers, 

### which must also be considered 

### in acosting 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 andservice 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 isto estimate the underlying cost and

### transit-time structure, calibrated against available rate and service data. This section examines the specific elements used inwater transport costing, provides some representative sources, and also identifies 

### critical issues 

### which affect the results. Total transport costs can be estimated from acombination 

### of physical characteristics, operating and 

### productivity fac

### tors, and 

### unit cost elements. 

### The physical characteristics relateto items such asvessel type, cargo handling equipment, and commodity density. Operating and 

### productivity factorsinclude vessel and cargo processing time, fuelefficiency, and

vessel speed.The unit costs are

### typically based onvolume 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 andother.

Vessel-related inputs encompass physical and cost characteristics

### which apply regardless 

### of the voyage

### or service patterns (with some exceptions). Thephysical characteristics of the vesselaffecting costs include:



## . type and 

### utilization (e.g., tramp 

### bulk vessel);



## . physical dimensions (for accessibility andport charges);



## . capacity (cargo load and design speed);



## . operating 

### efficiency (fuel consumption and maneuver

### ability);



## . manning requirements;



## . safety characteristics 

### (for annual 

### repair and insurance estimates); and



## . annual operating availability (for allocating annual costs).

The cost inputs associated 

### with thevessel include:



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



## . annual insurance 

### (hull and machinery, personnel and 

### injury);



## . maintenance and repair (periodic andoverhaul); 

## ' supplies and stores;



## . crew costs;



## . fuel; and



## . administrative/overhead. 125



### is defined 

### bythe specific 

### port calls, thevoyage length, and the time or distance under low-speed operations (e.g., canal 

### transit).Vessel itineraries frequently change, sometimes requiring adefinition of aprototypical voyage 

### for analysis. 

## A tramp operation 

## will typically assume 

### a direct route between the load anddischarge 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) legfrom 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 

### afixed 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 andother delays). 

### Transit time 

### for inland barge operations 

### may include delaysfor lockprocessing which often represents alarge por

### tionof 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 

### (infixed hours or days pervoyage), vessel berthing 

### time (basedon vessel size and berthtype), and cargo handling time (based on load charac

### teristics, equipment 

### type and stevedoring practices). The term 

### "port delay 

### " is

### typically used to denote an unusual 

### circumstance (e.g., berth congestion) 

### which extendsthe port time, but may be applied to the entire port time in some cases. Costswhich are specific to avoyage itinerary include vessel

### tolls andport-related chargesapplied tothe vessel (often based 

### on size) andthe cargo transferred 

### (mostly based on type andvolume). Vessel-related port costs include dockage and pilotage, while cargo-related costs include wharfage and cargo handling costs. Some 

### port costs arefees 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 andequip

### ment 

### 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, oftenwith no public port involved. Costsfor these operations must be estimated from private sources. Sources

### for voyage itineraries include 

### Lloyd's on-line databases, theJournal of Commerce Shipcards and other ser

### vice 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 aparticular port range. Fees charged by ports usually areavailable from port

### tariffs, although 

### ta¡iff rates may notapplyto high

### volume users.

### Worldwide 

### port directories (e.g., Lloyd's Annual costs for capital/lease, maintenance and insurance
are generally specificto aparticular type of vessel, but may "--' '.'ifl^ fLô rr¡ña ^€ "tili-.ti^- ¡'- n hifth riaL r¡n¡¡ococ\ YoJ vv¡r¡r \e.ó.r r¡¡6¡¡ r¡o¡\ rvJ 46vul. Supplies and stores andcrew costs can be estimated on an annual or daily basis. 

### Unit fuel costsvary

### withfuel type and the point of purchase. Administrative and overhead costs typ

### ically 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; andvarious industryjournals (such

as Contain erizatio n Int ernati onal, Lloy d' s Shipp ing E c ono- mist, Marine Log, Maritime Reporter and Engineering News, andWaterways

### Journaï¡. 

### As with other costs, costing 

### for international operations considers 

### the effect

## of currency exchange rateswhen appropriate. Voyage and port inputs are specific to aparticular useof a vessel, varying 

### by trade route andcommodity market. The vessel itinerary dictates many of the cost andtime factors and126



### 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 andpackaging characteristics. The physical stowage and load cha¡acteristics determine the 

### utilization of vessel andcontainer 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 handlingtime and costs, as

### well as load factors for liner operations and required vessel size

### for tramp operations (including backhaulopportunities). 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 thetotal costs for one-way pallets. Container system costs can beestimated 

### by calculating capital andoperating costs

### for afixed ratio ofcontainers to available vessel capacity 

### ("slots"). For example, 

### a service 

### offour vessels 

### with a capacity 

### of 800 twenty-foot equivalent container units (TEUs) might require three containers for every TEU ofcapacity or atotal 

### of7,20O TEUs. The 

### "additional" containers areeither resting in aport
or involved 

### in the inland transit. 

### A final cargo-related cost which may be considered isthe inventory costwhich measures the financial costof 

### "holding" the commodity during thetransit period. While this cost is not included in the ftansport rate ortypically calculated atall, it provides a basis for measwing the irnpact of delays and changes 

### in transittime, including changes resulting from changes in mode and./or route. Inventory costs can be calculated based on the average value 

### perweight

### or container 

### unit, combined 

### with interest rate which reflects the cost 

### of the capital required to hold the commodityfor the period of transport. Sources for cargo-related physical inputs include Thomas' Stowage (which provides weight-to-volume ratios on acom

### modity 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 statis

### tics (available 

### from PIERS, Census and 

### individual ports). 

### Other data 

### must be generated 

### from interviews 

### or general commodity orwater

### transpofation sources. Thefinal category ofcosts isfor inland transpof which can be estimated using the appropriate modal costing methodology 

### or assumed at afixed 

### unit cost 

### if not influenced 

### by the new

### facility. A key consideration for many general cargoes is thesplit between 

### rail and truckflows. This information is not generally available but may be collected through asurvey or interview process or approximated from adisaggregation 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 orsinglevoyage, and then appropriately allocating the costs over the

### trafficflow. Thefirst 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 avoyage-basis requires the 

### following steps:



## . calculate total annual costs;



## . estimate 

### projected annualoperating days (excluding maintenance andother downtime);



## . calculate

### daily cost by 

### dividing annual costs 

### by operat

### ing days; and



## . calculate voyage costs

### by multiplying daily costs 

### by voyage days. 

### Fully allocated 

### unit costs

### for bulk and breakbulk commodities are usually statedon a "per 

### ton" basis, 

### while containerized cargoes may be either perton or per container or 

### TEU. Costs may also be stated on aton-mile basis, particu

### larly 

### 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 resultswhen 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 adisparity between rates and

### fully allocated costsdue

### to the method 

### for allocating fixed costs. 

### It is often usefulto segregate marginal andfixed 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 andcontainerized commodities. It is

### crit

### ical that vessel 

### loading reasonably 

### reflect the cargo 

### mix, particularly when comparing different vesseltypes.



## . Allocation to 

### Backhaul 

### Flows-Many waterborne services are designed 

### for one-way movements 

### of specific com

### modities (e.g.,vehicles 

### or bananas) 

### or may havea natural imbalance 

### in one direction. The backhaul leg 

### is often considered secondary to the main cargoflow and is often sold on a marginal cost basis. 

### In such cases, an equal allocation 

### of fixed costs among all

### traffic understatesthe costs in the head

### haul direction. (The service 

### would probably exist

### with no backhaul 

### traffic, inwhich case,the headhaul 

### trafficwould be assigned allfixed costs). Various adjustments include assigningonly 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 feesand 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 sometypical costs for deep-draft vessels by type andvessel size.The costs per hour are aweighted averageof fiscalyear 1995 Corps of Engineers estimates

### for vessel costs at sea and in port andexclude port fees andtolls.3 (The 

### only difference between at-sea and in-port costs is

### fuel consumption, which represent less than ten percentoftotal costs.) For tankers and

### dry-bulk vessels, costs perton-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

Containerr.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 VessclCosis, 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 andsubstantial 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 arefiscal 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¡eTank 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 teamestimates and

### data from U.S.

### Army Corps

### of Engineers, 

### Institute ofWater 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 2.90year 1995 Corps of Engineers estimates.a The costs perton

### mile 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, anadditional (waterway dependent) adjust

### ment 

### is necessary 

### to reflect 

### locktransit 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-milevarysignificantly

### withtow size and,

### to a more moderateextent, 

### 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-bargetows are operated on several waterways, 

### and l5-barge tows are operated on the 

### Illinois and Ohio Rivers and onthe upper Mississippi (and 3O-barge tows are used on the lower Mississippi). Specialized barges 

### usually areoperated

### in dedicated service 

### using small tow sizes. For mostcommodities 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 afactor of ten. However, asobserved above, for most waterways, the barge costs require a

### further adjustment to 

### reflectthe effects 

### of lock delay andtransit times. Furthermore, 

### if comparisons areto be madewith 

### rail rates, anadditional adjustment is required to reflect the greater 

### circuity

### of waterways. (Barge 

### circuity is estimated to average about 17 percent more than 

### rail circuity.s)

AIRTRANSPORTATION 

### t29
analysis.) 

### Air freight costscan 

### be estimated based 

### on the 

### following categories of inputs:



## . flight andairport operating characteristics;



## . operating expenses (fuel andother);



## . 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 

### fromwhen 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 passengertransport 

### utilize detailed periodic costs for specific carriers, aircraft types and 

### operating 

### divisions as designated 

## in the Department 

### of Transportation 

### Form 

### 41filings and reports. 

### For example, Carrier

### A may

### file areport 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 oper

### ating expenses. 

### Aircraft-related 

### unit costs 

### include maintenance and capital cost (depreciation. amortization and leases) also state per block hour. Fuel costscould also use Form 41 consumption rates per block hour, combined

### with the appro

### priate 

### 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 becalculated on atrip basis,
as most carriers use contract operators at non-hub airports (due

### to the 

### limited number 

### of dailyflights). 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^ nnntractoperator, but could be estimated from charter rate quotes. No published source is available 

### for other ground costs. 

### Administrative overhead andprofrt 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 perton depends 

### highly onthe 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, aswell asfrom the manufacturer. As with vessel operations, it is critical that the impact ofvolumemeasured commodities on available capacity be considered. The secondary statusof airfreight in the airtransport indus

### try is indicated 

### by the 

### lack 

### of data andtechniques 

### for cost analysis.

### While detailed unit costs a¡e available for passenger hansport, 

### freight costsare

### typically stated as general "per pound" rateswhich areapplied to entire markets (e.g., U.S. to North Europe). Although integrated air carriers have detailed internal costing methods, they do notfile datathat is comparable to thatfiled by passenger carriers andthey use generaltar

### iff ratesthat are not easily correlated with specifictraffic flows. The 

### following discussion addresses general cost elements used 

### in air passenger costing asappliedto airfreight opera

### tions

### for a combination 

### or charter 

### carrier. (The dedicated closed systemsof 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. 5Herbert 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 avariety of other logis

### tics costs 

### (including inventory costs,stock-out costs, etc.) incurred by the shipper or receiver. 

### Any increase in

### TLC 

### for
use

### of aparticular mode can result 

### indiverting some

### traffic 

### fromthat mode 

### to competing modes, and any decrease 

### in 

### TLC can 

### result 

### in diverting some

### traffic from competing modes tothe mode in question. Potential 

### modaldiversion can be estimated 

### using either disaggregate data

### for asample of potentially affected movements or more aggregate data 

### inwhich the

### totalvolume 

### of such movements has been summarizedby one or more key variables, such as

### by commodity.The diversìon estimates
can bederivedfrom estimates of before andafter

### TLC, from absolute or percentage change 

### inTLC, 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 

### bytheAssociation 

### 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 modaldiversion 

### 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 asig

### nificant 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 ortruck costs. Truck costs are used (instead ofrates) because they are more easily estimated (seeAppendix F) and because the 

### highly competitive nature of thetrucking indus

### try causes

### trucking companies 

### to pass 

### both upward and downward cost changesthrough 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 INTRUCK 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 

### railton-miles that would result 

### from aone percent change intruck 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 andfor automobiles. Since 

### railtraffic now consists disproportionately 

### of the 

### latter categories

### of commodities, 

### the overall effect 

### of changes 

### in transport costs

### would be sornewhat less than aglance at 

### Exhibit G.1 

### might suggest. It is reasonable to presumethat the cross elasticities shown 

### in Exhibit 

### G.l represent the effects 

### of a reasonably 

### uniform change in truck costs;4 andthey 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. 2Transmode Consùltants, lnc., Truck-Rail, Rail-Truck Diversion Model, Users Manual, Draft, Washington, D.C., December 1994. 3J. Jones, F. Nix, and C. Schwier, The Impact of Changes in Road (Jser Charges on Canadian Railways, preparedfor Transport Canada by the Canadian Institute of Guided Ground Transport, Kingston, Ontario, September 1990,Table 4.2.
aThe 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 

### bycommodity

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 FinishedAII Else

Commodity RallTon-Mile Cross Elasticities 131

The "Canadian Tax 

### Policy" elasticity wasdeveloped 

### 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 Canadiantruck-tax 

### policy that 

### would decrease 

### overall 

### truck costs 

## by four percent or increase them by 9or 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 

### bydividing the resulting estimate 

### of the percentage increase in 

### railton-miles for thefirst policy alternative by the assumed four percent decrease in truck costs. Some internal inconsistencies 

### in the Jones, 

### Nix, andSchwier results6 leave
uswith somewhat lessconfidence in this elasticity than in the preceding setof 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 thefour setsof size-andweight analyses are the assumptions relatingtotruck 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 arelatively extensive setof major roads; and the lasttwowould alsoallow the

use oftwin 48-foot trailer combinations on the Interstate Sys

### tem and 

### on some 

### additional roads. 

### The last

### two analyses 

### differ intheir estimates 

### of the amount 

### of traffic that can be carried 

### efficiently on

### twin 48s. These 

### two sets 

### of diversion estimates alsowere adjusted downward 

### by Sydec 

### to minimize the effects 

### of some 

### limitations in the 

### ICM's ability to represent the network onwhich 

### twin 48s

### would be allowed to operate.e The 

### Exhibit G.2 cross elasticities show substantial varia

### tion 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

### intruck 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 onCantdiøn Railways, prepared for Transport Canada by theCanadianInstitute 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 useof elasticities by 

### individual com

### modity group 

### is the use

### of overall elasticities. 

### Exhibit G.2 presents six sets

### of overall crosselasticities developed 

### from published results using 

### ICM data. For eachsource,the elasticities show the effects of aonepercent change intruck 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 indicatesthatthe 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 elasticitiesshown

### 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.

5Scott M. Dennis, op. c¡t-, pp.7-9. 6Jones, Nix, and Schwier data actually can be used to derive both arailton-mile elasticity (1.00, asshown 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. 8Sydec, 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.
elbid.,pp. V-4-V-5. AIso discussed briefly in Section H.l ofthis report.

### r32



### Exhibit G.2. Implicit overall cross elasticities

### fromthe 

### ICM.

1. Unifom Chanç in Truck Costst 2. CanadianTax Policf

Size andWeightAnalys€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. 3Herb€rtWeinblatt, 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.

aSydec, 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 estimatedto average 3.6 percent andto be ashigh 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 

### uniformdistribution of the cost savings. The above discussion leads us

### to conclude that 

### uniform changes in the cost of operating combinationtrucks 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

### twoto three times as large.



### Cross Elastic¡t¡es 

### fromthe CNand CP RailTon-Miles R¡il Revenue

C¡oss Elasticities

0.52 1.00 tion intruck costs for the three scenarios (which ranged 

### from



### 8

## to 14 percent), Jones,

### Nix, and Schwierrr derived the 

### implicit crosselasticities shown 

### in Exhibit G.3. 

### The CP diversion estimatestended 

### to produce 

### slightly larger cross elasticitiesthan the CN estimates. More significantly, both sets

### of crosselasticities are appreciably smaller than those produced by the 

### ICM for the effects ofchanges 

### in truck size andweight limits. 

### At least part of the reason for the lower cross elasticities isthatthe 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 todiversion totruck. Other possible contributorsto the difference in cross elasticities could include tendencies 

### for the CN andCP analyststo have underestimated diversion or for the 

### ICM to haveoverestimated 

### 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 0.5for rail ton-miles and0.8for 

### rail
revenue. Separate cross elasticities were not obtained 

### for rail tons. However, most 

### railtraffic 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 totruck while the longest haul movements are more insulated

### from truck competition than more moderate-haul movements.) Therefore, the length 

### of haul 

### of newly diverted 

### railtraffic is 

### likely to be

### slightly higher than average, andthe cross 

### elasticity

### of rail tons is 

### likely to beslightly smaller thanthatof rail ton-miles. Hence, 

## it would appear appropriate 

### to assume 

### that

### for a uniform change intruck 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 1.0to2.3for 

### 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 

### towhich the changes arefocused on rail-competitive truck operations, and sothe extent towhich the cross elasticities suggested 

### in the preceding paragraph should be increased. Since 

### rail routes 

### usually are more 

### circuitous than 

### truck routes,

### the change 

## in truckton-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 aspart 

### of a 1981 study sponsored

### by the Roads andTransportation Association of Canada.l0 In that study, the two railroads provided estimated ranges for the expected effects 

### ofthree possible changes 

### in truck size andweight 

### limits on their 

### traffic volume and revenue. Using estimates 

### of the average reducr0 N.A. Irwin and R.A. Barton, Economícs of Truck SizesandWeights 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 onCanailian Railways, prepared for Transport Canada by the Canadian Institute of Guided Ground Transport, Kingston, Ontario, September 1990, Table 4.3.

change intruck ton-miles can be obtained by 

### multiplying the 

### rail estimate 

### by -0.85.''?



### G.2 EFFECTS OF CHANGES IN RAIL RATES AND RAIL COSTS RailTon-Miles Rail Revenue Cross Elasticities

0.39

### - 0.51 0.35

### - 0.59



### Information about the modal-diversion effectsof changes in 

### rail ratesandcosts 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 andshort-dis

### tance 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 insulatedfrom intermodal competition thantruck 

### 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 suggestedthat a

### uniform
one percent change 

### intruck costs would result 

### in diversion amounting to about 0.5 percent of 

### raii ion-miies anci 0.4 percentof 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 percentof rail tons; i.e.,that the own elasticities of rail ton-miles and

### rail tons 

### to changes 

### in rail ratesareabout 

### -0.75 and 

### -0.6 respectively. (These elasticities are negative since an increase 

### in rail rates

### will result 

### in adecrease 

### in railtraffic.) The elasticities suggested 

### in the preceding paragraph are appropriate when changes 

### in rail costs and rates are reasont33
ably 

### uniform acrossall categories 

### oftraffic. Changes 

### in rail coststhatapply 

### primarily totruck-competitive 

### traffìc (most 

### of which provides 

### relatively 

### low operating 

### margins) are 

### likelyto produce rate changes 

### that are concentrated on 

### this 

### traffic. As 

### in the case 

### of changes 

### in truck costs, rate changes that areconcentrated on modally competitive 

### traffic
are 

### likelyto produce substantially 

### higher elasticities than 

### uniform changes 

## in rates,

### with the highest elasticities (perhaps in the 

### two tofour 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 

### afunction 

### of calculating



### ,.. 

### =

# lgL " dP8 If thefunctional relationship between quantity and price is not available, then 

### it is generally not possible to calculate a point elasticity.



## . An erc elasticity iscalculated from information onprice andquantity before and after aprice change 

### logQ,

## - logQ 

## "': lo¡Pr-logn
This measure most nearly approximates apoint elasticity.



## . A shrinkage 

### factor also is calculated 

### from information
on price andquantity betore andafter aprice change @,

## - 8,)lQ, ""

# = çp,

## - e,v r,
The problemwith calculating elasticities asshrinkage factors isthat

### if the price is reduced by agiven amount and then increased by the same amount, the shrinkage factor does not predict thatdemand

### 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 beshort-run or long-run in nature, depend

### ing on the

### time period 

### overwhich changes 

### in demand are

r2The results of two ICM analyses indicate that, on average, rail routings are I6to 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 elas

### ticities 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, 

### theonly 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

### offreight demand 

### include the scale

### or output effect associated

### with achange 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 modalsubstitution 

### issues-i.e., an estimate of how much 

### traffic

### will be shiftedfrom one modeto another as aresult 

### of

agiven price change 

### in oneof the modes. The compensated elasticity models provide estimates of the specific measures of interest tothe analysts. There have been 

### two major approaches 

### in collecting the data needed for developing estimatesoffreight demand elas

### ticities. One involves the collection 

### of aggregate data to develop 

### elasticity estimates. Specifically, these studies 

### rely
on data collected in the aggregate for aparticular geographic 

### corridor (state-to-state

### freight shipments,

### for example) on average freight rates, shipment volumes, shipment times, and delivery 

### reliability by mode. Depending uponthe particular 

### analytic model used

## to develop 

### elasticity measures, the dependentvariable

### 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 modalvolumes. 

### If the translog model is used, the dependentvariable is average 

### freight rate 

### on the geographic 

### corridor 

### for 

### all modes. Oum analyzed avariety 

### of functional forms 

### for developing 

### elasticities 

### from aggregate 

### dataand concluded

### that the translog model performed the best 

### inall aspects.r6 When the 

### policy analyst isfocusing on total

### traffic volumes and modal shiftsoccurring as aresult ofthe implementation of apartic

### ular 

### policy, then elasticities 

### from aggregate models seem most appropriate. The second class 

### ofmodels requires researchers tocollect data 

### from a representative sample 

### of individual shipments. For each shipment, 

### information is collected on freight rates for the mode used as

### well asalternatives. Data are also accumulated on time and

### variability ofthe shipment by the mode usedand 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 aregrounded 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 easyto obtain because of the con

### fidentiality 

### 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 devel

### oping 

### 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 

### istofind a set 

### of elasticities that can beemployed when ananalyst wishes to estimate the demand impact of modal 

### coslprice increases resulting 

### from the 

### initiation of a new 

### policy. As Oum has argued, thedisaggregate and aggregate approaches should 

### beviewed as complementary, not competing.2o The analyst must be aware 

### of the 

### limitations in the entire set 

### offreight demand elasticities that have been developed

15Tae 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. róTae Hoon Oum, "Altemative Demand Models and Their Elasticity Estimates," Joumal ofTrønsport Economics and Policy,May 1989, p. I85. 17Clifford Winston, "A Disaggregate Model of the Demand for Intercity Freight Transportation," Econometrica, Vol.49, No.4 (July 1981), p. 981. ¡8Cliffo¡d Winston, "A Disaggregate Model of the Demandfor Intercity Freight Transpofarion," .Econometrica,Yol.49, No. 4 (July 1981), p. 998.
reBoth 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. 20Tae 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 haveoccurred in thefreight transport sector since dereg

### ulation. There 

### were

## aflurry 

## 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 car

### riers and shippers and 

### kept out 

### of the 

### public domain. For example, more and more 

### of the records

### fromthe Railroad 

### Waybill Data 

### do not include rate 

### information because the rates are negotiated between the parties andfiled as "contract 

### rates."There 

### is no question 

### that the transport sector has changeddramatically since passage of the Motor Carrier

### Act of 1980 and the Staggers RailAct 

### of 1980. As aconsequence, reference 

### to elasticities based 

### on pre-1980 data 

### must be subjected tocareful 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 pointtoward 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¡ketplacethan 

### 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 tothedevelopment 

### of demand elasticities. The aggregate studies suffer 

### from their 

### inability 

### to model the actual 

### modal decision process.

### All individual decisions are 

### lumped together 

### andthe 

### 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 betaken out 

### of context 

### from ashipper's overall modal assessment process. Thus, shippers 

### might make modal decisions based on

### their entire set

### of shipment needs over aparticular time 

### periodquarter, 

### half-year, or year. 

## It may 

### not be

### very effective to base demandelasticities on an 

### individual shipment 

### from a particular shipper rather thanfrom that shipper's entire set

### of shipments. Despite these 

### very significant 

### limitations, the 

### policy ana

### lyst 

### still must address the issue 

### of how

### to estimate

### freight demand impacts associated with the implementation of a

### policy with aquantifiable impact on modal costs.The next section

### will presentfreight demand elasticity estimatesthat represent 

### the best available estimates. 

### There

## will be strong caution that these estimates have some very significant 

### limitations. Nevertheless, the 

### policy analyst may needto evaluate them asthe 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 oftwo computer 

### models

### that are designed

## to estimate 

### modal diversion between 

### rail andtruck using disaggregate data on 

### individual shipments and a

### third model designed to estimate diversion using more aggregate data.

H.1 INTERMODAL COMPETITION MODEL


### The mostcommonly used 

### tool

### for estimating 

### rail/truck modal diversion 

### from disaggregate data 

### isthe

### 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 aresult ofcompetitive railroad rate reductions, andwhich 

### 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 truckto 

### rail (using 

### a sample 

### of truck movements 

### from the 

### North American 

### Trucking Survey).3 The proprietary nature of the 

### ICM makes acareful evalu

### ation 

### of the accuracy 

### of its estimates

### difficult. We have 

### reviewedoutput produced 

### by the previous 

### version

### of the 

### model and concluded 

### that the cross-elasticities

## of rail demand relative to changes 

### intruck 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 aCN/CP analysis discussed inAppendix G suggests that the 

### ICM may tend to overestimate diversion mod

### erately. 

### 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 bethe same as current 

### utilization

### of 48-foot semis; i.e.,that

### all loads carried would be loads 

### for which the vehicles are designed andthatthere would be no 

### increase

## in empty mileageand 

## no decrease

## in annual mileage. These assumptions about 

### utilization areoptimistic, especially 

### with respect 

### to non-door-to-door configurations such astwin 48s. The 

### ICM's estimates of costsavings resulting from the useof 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 exoge

### nously specified estimates 

## of the effects

## of regulatory changes ontruck transport costs;s and adjustments also can be made to 

### ICM results 

### (with some lossof accuracy) to compensate 

### for any known tendency 

### of the 

### model

### to over or underestimate diversion. Several otherfactors 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.Thesefac

### tors are listed 

### below, along 

### with estimates 

### of the 

### effect

### of these factors on the model's estimates ofoverall diversion to 

### twin 48s.6



## . Fuel taxeswere assumed to be zero on

### alltruck movements

### originating 

### in Canada, increasing 

### overall diversion by an estimated 8.0 percent in thetwin-48 analysis andto an unknown extent 

### in other model runs.



## . The costs

### of reconûguring 

### twin 48s and

### the costs

### of

access hauls to thetwin-48 network were not adequately reflected 

### for short hauls 

### (particularly those under 800 

### miles) 

### while they were overestimated 

### for long hauls (particularly thoseover 1,800 miles), increasing overall diversion 

### by about 23 percent.



## . Because the 

### ICC 

### waybill sample does not 

### identify the 

### true 

### origin andtrue 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. 2The ICC Carload Waybill Sample consists of a systematic sample of waybills for railroad shipments terminating on Class I railroads in the United States. 3The North Amedcan Trucking Survey is a survey of truck drivers conducted during 1993and 1994 af 46truck stops byAfthu 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. slbid.; 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. 6Sydec, Inc., Transmode Consultants, Inc., and Jack Faucett Associates,Analysis o/ Innger Combination Vehicles, Final Report, preparedfor 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 

### railtraffic that is intermodal 

### or

### will be 

### inthe

### future. Since intermodal 

### traffic is the 

### traffic most 

### readily divertible to

### twin 48s,

### this understatement tendsto reduce overall diversion to

### twin 48s.



## . The 

### ICM estimates 

## of other logistics costs (OLCs) 

### (which have been printed 

### inthe 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 ofthis 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 thefirst 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 theway 

### 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 

### NATSfor estimating diversion from truckto rail resulting from policy changes that increase truck costs or reduce rail costs, afailure to adjust for this bias

### will result in significantly overrepresenting longhaul truck movements, which are relatively divertible, and so in overestimating diversion from truckto rail.

H.2THE 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 

### fromtheir intermodal origins and destinations, County Business Pattern data,and agravity 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 net

### work 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 arail/truck circuity factor results 

### in consistent estimates 

### of rail and

### truckO/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 

### fromthe 

### 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 biasesthe analysis toward 

### railto-truck diversion. 

### AII intermodal shipments are assumed to be madethrough
one of 32 major intermodal 

### railterminals at each end oftheir 

### 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.tthe T-R-/R--T Model rsnonproprietar,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 derivationsfor the many parameters incorporated in the model areprovided (though some 

### of the parameter values can be inferred from three pages of out

### put 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 todevelop 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). TheTruck-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 Administra

### tion (FRA).

### A preliminary description 

### of this model is contained 

### in a

### Draft Users Manuals released

### in December 1994. The actual model and asomewhat revised Users Manual are scheduled for release 

### in the next few months. 

### TheT-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 thantruckload 

### (LTL), longer

### combination vehicle 

### (LCV), and 

### private); threetypes

### of intermodal transport (trailer-on-flatcar, doublestack, and Road Railer); andconventional 

### rail carload transport. The

### T-R/R-T Model represents nearly 

### all movements as 

### originating andterminating at county seats. The actual origins and destinations 

### of shipments currently being made by 

### truckor conventional 

### rail are contained 

### in the data sources

1lbíd., p. C-7

. 8Transmode Consult¿ìnts, lnc., Truck-Rail, Rail-Truck Diversion Model, tJser Manual, Draft, Washington, D.C., December 1994. , Ibid138

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 asingle shipment

### (of

a

### weight-limited sodium compound). 

### The most significant concerns relate 

### to the analysis 

### of LCVs. Data contained 

### in the appendix indicatesthattransit 

### times

### for LCVs are assumed 

### to be one-third shorter than thoseoffor-hire 

### TL transport, and that 

### reliability is assumed to be20 percent better. Although not discussed anywhere 

### in the Users Manual, the shortertransit times reflect an assump

### tion 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 opera

### tion and 

### the single-driver operation assumed 

### for conven

### tionaltrucks. 

### (If the costs actually aresimilar, conventional 

### TL operators 

### would choose

### to provide the better service attainable 

### with relay operation.) 

### The transit 

### time assumption 

### for LCVs apparently also ignores the delaysthat can be expected at staging areas 

### in order to match pairs 

### of trailers 

### moving 

### inthe same general direction. Also, because of the need for such delays 

### (without which the economies of 

### LCV operation are unattainable),

### it

seemsthat, for most shippers, transit-time 

### reliability of 

### LCVs would be poo 

### rer than that of conventional truckload service (though someshippers might be

### willing to pay apremium to guarantee expedited handling oftheir 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 totruck.



## . A load ratio (loaded 

### miles pertotal 

### 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 abackhaul.)



## . The assumptions used 

### for 

### LCV access costs (roughly 

### half to 

### two-thirdsof 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 anddelivery.



## . A negative chargefor pickup and delivery appears to be incorporated into the rate structure of truckload carriers (actually, a$162 charge pershipment

### 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 

### priorto public release 

### of the model. However, addressing other concerns 

### will require a larger 

### effort thanthe one that 

### is currently underway. 

### Accordingly, 

### we are 

### not confident 

### thatthe

### first version 

### of the 

### model

### will be appropriate 

### for analyzing modal diversion.

H.3THE l,OOO.MILE STRATEGIC CHOICE MODEL 

### The 1,000-Mile Strategic Choice 

### (TMSC) Model cur

### rently 

### is being used 

### by Mercer Management Consulting 

### (MMC) to perform 

### truck/rail modal diversion analyses as a part

### of astudy being conducted 

### for the Southern 

### California Association 

### of Governments (SCAG). The model is proprietary and very 

### little information iscurrently 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 thetwo 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, andaccuracy 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 asurvey

### 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 betoo naffow to be used

asthe 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, preparedfor the Southem California Association ofGovemments, Los Angeles, April 1995.

# APPENDIX I



## CASE STUDIES

This appendix presents two casestudies demonstrating the practical application 

### of some

### of the procedures presented 

### in this Guidebook. The first case study presentsthe development of forecasts offreight 

### traffic for North Carolina's twoexisting freight airports and for athird proposed freight airport. These forecasts make use 

### of the comparison and 

### proximity/level-of-service procedures presented in Chapter 4 under Evaluating 

### Proxim

## ity and 

### Level 

### of Service, 

### andAnalyzing 

### Total Logistics Costsof 

### Individual Shipments. The second case study presents an analysis 

### of the effects of a possible change in federal truck size andweight 

### policy. This case study demonstrates some of the cost estimation and modal-diversion procedures presented 

### in Appendices 

### F-H.

1.1CASE STUDY: NORTH CAROLINA FREIGHT AIRPORTS 

## h L99l, the State 

### of North Carolina's Department 

### of Transportation commissioned astudyto 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 stateairports. 

### The study also evaluated the technical and market

### feasibility of the Global 

### Air Cargo 

### Industrial Complex (GACIC) concept 

### which is currently being developed asthe Global Transpark 

### in Kinston, 

### North Caroiina. The 

### GACIC anaiysis inciudeci a pro

### jection of new industrial 

### activity attracted tothe

### 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 andonethat 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 exist

### ing and projected infrastructure. The 

### GACIC portion 

### of the study required the 

### ability to define anew 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 ontrend analysis, projecting future growth based
on national trends and acontinuation of historical growth.

### A primary reason 

### for this strategy has been the 

### limited avail

### ability 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, incor

### porating regional demand and ma¡ket share analysis. The
reasonsfor this more detailed analysis included:



## . arequirement toforecast flows among multiple airports which share aconìmon hinterland;



## . a requirement to testvarious scenarios

### for the location and service 

### profilefor 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 

### inthis case included measuring baseline 

### activity and relationships andprojecting them 

### into the 

### future under various development scenarios' The 

### North Carolina 

### air cargo market analysis isolated 

### four primary areas of dataandactivity:



## . 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

INorth Carolina Air Cargo System Plan and aGlobql 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 thevolume, 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 requirementto 

### 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

### forvarious data sources.
Since 

### allof the state's airports were included, the 

### "local" market was defined asthe 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 

### atthefour-digit SIC level)

### by U.S. county.2 The 

### "regional" market was defined as an aggrega

### tion 

### of various 

### "airport market areas" 

### which are county groupings surrounding primary airports asdefined by Colography. The

## "All Other 

### U.S." market was defined asthe rest of the national market as measured

### for all airports. 

### The primary technique 

### required 

### for the geographicalbased data was associatingthe detailed county-based data 

### with more aggregated data atthe state 

### or other levels. For example, the employment forecasts

### of the Bureau 

### of EconomicAnalysis 

### (BEA) areonly available ar rhe two-digir SIC industry level by statewith 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 averageof dataavailablefor the larger regions. 

### As market 

### proximity is a 

### key factor 

### in determiningair cargo routings, the 

### location

### of market 

### origins and destinations relative to the study's airports was incorporated ashighway 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 

### marketwas estimated

### from the 73 industry totals using expansion factors provided by Colography asdetermined from national 

### traffic totals. Inbound 

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

## "All Other 

### U.S." demand 

### totalswere 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 manu

### facturing, tradeand transportation markets. Market growth 

### will incorporate national, regional and local economic trends. Employment growth trends were usedto 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 useof 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) andthe 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 atrend 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 produc

### tivity growth rates used 

### in the forecasts

### for regional outbound shipments.



## . Regional growthfor outbound shipments was compared 

### with national 

### growth as projected 

### inthe Boeing Com

### pany's World

### Air Cargo Forecaslsa resulting 

### in traffic projections 

### for the

### "All Other U.S." category.



## . Inbound 

### traffic estimates assumed the baseline distribu

### tion 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 anddestination 

### of traffic andthrough routing information. This study attempted to correlate airport 

### traffic volumes with the underlying demand and supply markets 

### in orderto 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. 3U.S. Department of Commerce, Bureau of Economic Analysis, BEA Regionat Prcjections 1o2040, U.S. Govemment Printing Office, October 1990. aBoeing Commercial Aiçlane Group, World Air Cargo Forecast, Seattle, annual. 5Federal Aviation Administration, Airport Activ¡ty Statusof Certifcated Air Carriers, anntal. 6Airport Operators Council Intemational, Worldwide Airport Trafrc Report, annùal.Exhibit 

### I.1. Market demandcharacteristics.

Geographic Regions

Data Item

Airport-Market Distances

OutboundAir Shipments by Industry Group and Origin Market Area

InboundAi¡ 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 ofsurrounding 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 WorldAir Cargo Forecasts Data source only includes top 70+ industries (at4-digit SIC level). Expansion toall induskies basedon Colography-supplied expansion factors modified basedon 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-NCtraffic based on outbound distribution. Total UStraffic estimated from Census statistics.

U.S.Airport Traffic (Baseline)

StateAirport 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 PrimaryAirports' Traffic (Forecast) TechniouedCommentg

Basedon 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 asthe residual.

North Carolina 

### Air Cargo Forecasting and Allocation Model Airport proiections match market demand forecasts with pro¡ected shrfts rnshaie (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 andtechniques used include:



## . Comprehensive international data was

### only available 

### in the Census statistics 

### for all airports 

### in the 

### V/ilmington, North Carolina, Customs 

### District whichfortunately correspondeddirectly with the state's airports. International estimates

### for individual airports required amatching

### of carrier and

### aþort data

### with the aggregatetotals.



## . The 

### FAA statistics exclude 

### international carriers and certain all-cargo operators, and 

### only measure enplaned cargo.



## . The 

### AOCI statistics incorporate 

### filings by memberairports which are notverified. Statistics provided by 

### individual airports were used 

### toverifythis source 

### for the study.



## . The definition 

### of international cargo used

### in the ma¡ket demandforecasts 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 atransshipment 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 theforecasting model. Service levels can be measured 

### in terms of total cargo capacity modiûed to reflect the

### timing andtype of capacity. The study team derived relative service levels 

### forboth the domestic and international markets basedonthe 

### interview process and a comparison of

### freight-flow patternswith 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 andcarrier operating patterns. 

### Airport traffic flows were compared 

### with market demand to establish baseline cargo routing patterns which were then projected andapplied tofuture 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 maintainthe same share 

### of the projected state total throughout theforecast period. Forecasts 

## of aircraft activity 

### at primary airports were required

### for the capacity analysis, as

### well asto sizethe 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 assumedto be the same. Average cargo loads 

### per

### flight operation were derived 

### for both types 

### of flights 

### with passenger patterns assumedto 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 equipmenttechnology andfleet 

### mix. Exhibit I.5 shows how aircraft operations were projected.

Cargo Routing Patterns

The interaction between market demand and

### facility activ

## ity can be represented 

### by cargo 

### routing patterns which describe the

### facility's shareof 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 toderivetraf

### fic 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 stateairport 

### traffic which originated 

### in North Car

### olina. These results were 

### modifiedto include international shipments 

### loaded 

### on domestic 

### flights 

## for transshipment at another U.S. airport 

### (trafficwhich 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 

### regionswas 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 shareforecasts were based on the study team's analysis of historical trends and interviews with industry participants concerning future service development plans. Baseline shareswere estimated for 2000 and 2010 and matchedto 

### projected demand 

### totals 

### in order

### to forecast 

### totaltraffic. Exhibit L7 shows the structure used 

### for these forecasts. Forecasts 

### for the 

### primary cargo airports 

### utilized a cargo routing model which allocated the assigned statetotals based on acombination of

### proximity and service levels. The struc

### ture

### 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 Colog

### raphy baseline data base 

## is incorporated

### within the model (shown as the top 

### half of theexhibit). 7U.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 ofTotal Domestic International Total
hùourd as

### a Percent ofOutbound 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 bytheir distance to the 

### regional origin/destination 

### (O/D) "zones" (North Carolina county or regional market area) andtheir relative service levels 

### for the domestic and international mar

### kets.

### The airport

### with the highest service 

### level was assigned avalue 

### of 100 percent 

### with other airports' values setrelative to that level. Forecastvalueswere set rel

### t.

2. International inbound traffic 

### estimatd as 739.2Vo of outbound 

### traffic based on the ratio of 1990Wilmington import weight to reported internationalwaybill exports. Total inbound traffic estimated as percentage of outboundtraffic. 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 orterminating 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 FAAÀirport 

### 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 ofaircraft operations at the North Carolina commercial airports.

Total Cargo Traffic (ü)0 Tons) (-apo Activitw - Passe¡oer (}mati<rns Allocation ofTotal Traffi c Trafûc (000 Tons) Numberr of Flight Operations (000)

F'nninna¡l Miv- All Caroo ()ncrafions
Percent ofTotal 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 ofTotalTraffic Traffic (000Tons) 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 E61Exhibit 

### I.6. Cargo routing pattern characteristics.

Total NC Airports' Share of Outbound Traffic by Market 

### Origin (Baseline)

Data ltem

Total NCAirports' Share

## of Study Team Inbound Traffic by Market Origin (Baseline) Total NCAirports' Share of Total Traffic by Market Origin (Forecast) Census State of Export and Foreign Trade Statistics
BE^A,, LocalArea Personal Income Study Team Sou¡ce

Primary Airports' Share of State Airport Traffic by Market Origin (Baseline/ Forecast)



### distance

### weighting factorswere varied

## to calibrate model results to actual baseline market shares.



## . Projected cargo volumes are 

### utilized 

### by the airport 

### activity modulewhich estimates 

### flight operations.

The 

### model's summary 

### inputs and outputs areshown 

### in Exhibir I.9.

New 

### IndustrialActivity

The proposed 

### GACIC 

### facilitywould operate as a general caron 

### ¡irnort âtfrâctino 

### resional caroo haserl on 

### nroximitv ---^--____Þ _-Þ_-__-- '-- Í- "--'---'J and service levels, aswell as amagnet to new industrial 

### facilities attracted 

### by the integration of industrial and transporta

### tion capabilities.The forecasting 

### model was designed to accommodate anew 

### "primary" airportwhich would compete for regional cargo which includes new

### traffic assigned tothe 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 Shareof export traffic originating in NC estimated using Census pattems for international flight enplanements averaged with study team estimates for other types of traffic. Shareof exports from RMAs estimated using five-state Censustotals with RMA portion basedon counÇ-based manufachrring earnings. Share of domesticoutbound traffic based onstudy 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 assumedthat 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 andtrans

### portation characteristics 

### for each 

### industrywas based on baseline year 

### activity for those areas. The number of new plants assignedtothe 

### GACIC area 

### in the forecast period was estimated 

### from the current 

### level

### of activity

### for the 

### prototype areas. 

### For example, 

## it was estimatedthatthe 

### GACIC could attract 32 electronic computer 

### facilities 

### by theyear 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 concern

### ing 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 theexisting state airport system and

a new 

### facility concept 

### with no available prototype. The techniques used incorporated awide variety 

### of data sources and attempted to 

### profile the relationships 

### involved 

### in air cargo marketsaccurately.Typical problems encountered included the synthesis 

### of data

### with varying levels

### of detail anddefinsThe 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 % ofTotal Toof.NlO/Ds
Resional Market Area Domestic International TOTAL Domestic Voof.Toþ| 

### %ofAllO/Ds AllOther U.S. Donestic International TOTAL Domestic Voof.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'iceWeights by PNCCA (D/f)

Ma¡tct tæalage Percenages

### fc Secondary NC andExhibit 

### 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 (ü10Tons) 

## 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-NCAirports Percent of TotalTraffic Leakage

1.56% 0.00% 0.00%

100 50 25 10 AverageAnnual 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€castPeriod

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"

2010Exhibit 

### 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 74.3 

### 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 \oExhibit 

### 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 ofthe United States FAA Airport Impact Study Industry Interview Program StudyTeam Assumes GACIC would attract high technology companiesdependent on air cargo similar to SiliconValley plus expansion of industries already active in North Carolina. New plantswould 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 andair cargo productivity per employee in 1990.
14from SiliconValley and 8from North Carolina (atfourdigit 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 oftransportation/public utility sectors to manufacturing s€ctor.
Toùal employment impact on sÞte estimated using multipliers from FAA study (Measuring the Regional Economic Significance ofAirports, October 1986). Colography Group StudyTeam Growth in outbound cargo tons per employee for San Francisco market
area (at

### twodigit SIC level)from 1983to 1990 extrapolated to 2000 and 2010 and appliedto 1990 averages for SiliconValley/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 tooutbound volumes. OExhibit 

### I.11. Summary of air cargo produced by the GACIC andthe economic impacts ofGACIC activities.

Cargo Impact (Tons) New Traffic by Industry Type -Outbound Manufacturing Supporting Industries

Domestic Percent ofOutbound 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 wasdesignedto

### 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.

1.2CASE STUDY: TRUCK SIZE ANDWEIGHT POLICY


### Truck size and

### weight regulation 

### is an important 

### policy issuethat 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 thesevehicles, and usually on shipment sizes andvehicle-miles oftravel 

### (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

### fortruck and 

### rail transportation and

### for associated 

### logistical functions, andthe financial 

### viability 

### of affected 

### truck and 

### rail carriers. 

### For most 

### potential policy changesthedirection of the various effects is

### fairly obvious, buttheir 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 desir

### ability 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 estimatesfor aspecified 

### policy change 

### of some interest is presented below. 

### A thorough analysis of the effects of this 

### policy changewould usethese assessments asthe basis

### for developing additional estimates

### t52

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

### viability ofcarriers.

1. Define 

### Policy

The 

### first step in theanalytic process is the development 

### of

aclear 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 capon grossvehicle 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.Thisformula 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 aseries of studies of possible changes 

### in truck size andweight limits.e

2. Base-Case 

### Forecasts These sources were usedto produce forecastsof

### VMT and truck payload ton-miles

### for 1995 bytrailer and axle configu

### ration, region, 

### GVW range, and 

### highway system (distinguishing ISvs. non-IS and rural vs. urban).

3. Gost 

### lmpacts

Thethird step in the analysis isto estimate the effect of the 

### policy on transport costs and,

### if necessary, on other logistics costs. For this purpose, adetailed 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 asthe 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 asubsequent study. Excerpts

### from the updated working paper are presented 

### inAppendix F. 

### The analysis 

### also considered 

### extra per-trip costs 

### for assembling and disassembling double-trailer confi gurations (estimated 

### in 1988 dollarsto be$30 per 

### tripfor vans and $15 per

### tripfor dumptrailers), and

### for cleaning extra tank

### trailers (estimated to average $100 per 

### tripfor chemicals and $20 per

### tripfor 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 

### atthe higher weight 

### limits being considered. Separate estimates 

### of the extent to 

### which carriers are 

### likely to change configurations were developed for carriers operatingdifferent trailer types. These estimates were based on the Step 3costestimates, information obtained in a series of car

### rier 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 onthe relative usage

### of the 

### differenttrailer 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; andthe 

### varying effectsthat these 

### opportunities would be The second step

### in the analysis isto 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;



## . TruckWeight Study dataon averageoverall 

### 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. II

eThis 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 PolicyAnalysis 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 I990. I0Sydec, 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 HighwayAdministration, 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. t3lbid., vpdated, October 1991.Exhibit 

### I.12. Relative efïiciency of alternative configurations for weight-limited truckload linehaul operation.

6Axle48 Foot Semi 5AxleTwin 28

9 AxleTwin 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 dunptrailers; 48feet for other 

### traller types. Source: Herbert Weinblatt, The Effectof Size

### anilWeight 

### Littits on Truck CosÉs, Working Paper,

prepared by Jack Faucett Associates forthe 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 usedtodevelop estimates of theover

### all 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 theway 

### inwhich 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 atweights above 80,000 pounds under the pol

### icy being considered. 

### For each

### configuration,

### this exhibit shows the estimated percentage difference between linehaul costs per ton-mile for typical truckload operation ofthat con

### figuration when 

### it is loaded

### to the indicated 

### GVW and the corresponding costs

### for a

### five-axle semi loaded 

### to 78,000 pounds, a 

### practicalweight 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 werederived 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 assumedto be operating at the indicated 

### GVW. The estimates shown 

### in Exhibit I.12 indicate that, except 

### for reefers anddumptrucks, 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 madeto semis

### with 36-foot 

### trailersthat are 

### limited to a

### GVW of 70,000 pounds 

### by Bridge 

### Formula 

## B. (The length

## of dumptrailers 

## is limited by stability considerations.) 

### Exhibit 

## I.l2 also indicates that linehaul operation

### of sixaxle 48-foot semis atthe Formula B 

### limits is sixto nine percent less expensive per

### ton-mile than operation 

### of five-axle semis,andthat 

### linehaul operation 

### of five-axle

### twin 28s at these 

### limits is about nine percent less expensive for dryvans and three percent less expensive 

### for reefers. Five-axle 

### twin 28s are included 

### in Exhibit 

## I.l2 and discussed 

### in this Case Study becausethe 

### possibility 

### of allowing thesevehicles 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 

### thefive-axle semis and the 

### alternative configurations; 

### i.e., there 

## is no difference 

### in annual mileage, empty rnileage, orthe extent towhich loads carried 

### fall shortof those thatwould beallowed under Bridge 

### FormulaB. Under the

### policy studied, however, many origins anddestinations cannot be served 

### by vehicles operating at 

### GVWsover 80,000 pounds. 

### To the extentthat a vehicle designed 

### for operation at a higher weight would be usedto
serve such an

### origin or destination, thatvehicle would not be utilized 

### fully. As can be seenfrom 

### Exhibit 

### I.l2,itcosts more to use such avehicle for weight-limited haulsto orfrom such shippers than to use a

### five-axle semi. The increased cost is two tofive percent 

### for six-axle semis

### of various bodytypes, and is substantially higher

### for the heavier configurations. 

### The above discussion indicates 

### that carriers 

### of weight

### limited cargo 

### would be able to benefit 

### from removal 

### of the IJThe 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 semistovarious heavier configurations

### providedthey cando 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 becanied 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 reduc

### tion in 

### utilization to negate the savings obtained. Hence, the transport-cost savings that result from removalof the 80,000 pound 

### GVW capwould be affected signifrcantly by the num

### ber

## of origins and destinations 

### that can 

### be accessed 

### by vehiclesweighing more than 80,000 pounds.

Responses by Typeof Operation


### As the above discussion indicates,the 

### ability of acarrier 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 orexclusively to carry cargo at or closeto the 

### Bridge Formula 

### GVW limits. This 

### ability depends on,the cargo 

### noûnally carried 

### by the 

### vehicle andthe 

### origins and destinations served. Consider operations that 

### involve weightJimited carriage between 

### a relatively small number 

### of locations. Such an operation might beconducted by aprivate carrier between its plants and warehouses, or 

### by acontract carrier transporting natural resources to any

### of severaltypes 

### ofcollection points (processing plant, railhead, grain elevator, etc.). 

## If all locations can be served by vehiclesweighing morethan 80,000 pounds 

### with little or no increase in 

### circuity, or

### if aportion 

### of thefleet can be dedicated to serving locations

### with this characteristic, aswitch to vehicles designed 

### for operation above 80,000 pounds would prove desirable. However, a change 

### in equipment generally would not be advantageous 

### for opera

### tions 

### ofthis type when 

### any significant amount 

### of service is required 

### to or 

### from locations that cannot be accessed 

### by vehiclesoperating 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 afor-hire truckload carrier that usesa single fleet

### of vehicles 

### to serve a large (and frequently changing) number

### of shippers. For such operations, replacement offive-axle semis by vehicles capableof operating at GWVs above 80,000 pounds would prove cost effective only to the extent that the newvehicles could be used primar

### ily to carry shipments thatthey can carry moreefficiently than
canfive-axle semis, and also tothe extent that such shipments
can beobtained without any reduction in annual loaded miles. This typeofoperator 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 conflgurationswould not besufficient tocarryall 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 ofthis subsection discusses the preferred confrgurations 

### for several 

### different types 

### of operation when

### the 80,000 pound 

### GVW 

### limit is not afactor 

### invehicle 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 anine-axle

### twin 28.The use 

### of doubles 

### for carrying chemicals and manyfood products, however, would be 

### limited both because the use of 

### twin 28s would increase tank-cleaning costs, and, in the caseof chemicals, because many receivers want only a

### limitedvolume 

### in any one

### delivery. Accordingly, the preferred configuration 

### for chemicals and many 

### food products 

### would usually be a six-axle 48-foot semi or afive-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 andthree-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 nor

### mally are no longerthan 36 feet, and Bridge Formula 

### B currently limits semis using these trailers to 70,000pounds 

### GVW. On aper ton-mile basis, Exhibit I.12 shows a21.5 percent linehaul cost savings obtainable by switching to nine-axletwin 28s. 

### Twin dump 

### trailers would have 

### to be disassembled 

### for unloading (at anestimated cost of about $15 per

### trip), and, 

### in
some cases, 

### for loading as

### well. For this reason, semis and single-unit truckswould generally

### still be preferred for hauls of less than 25 miles and

### for some longer hauls aswell. Flatbed operators would be 

### likely to prefer heavy doubles for much oftheir operations where access restrictions do not 

### inhibit the useoftwins, 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, aswitch to six-axle semis is attractive even forvehicles 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 extraaxle (or, alternatively, the

use of aspread tandem) usually is necessary in order to make 

### it practical to approach a

### GWV of 80,000 pounds while car

### rying 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 totruckload operators of dry vans aresomewhat lesssignificant. 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

### tovery 

### few operators 

### of dry 

### vans. However, 

### both six-axle semis and five-axle twins 

### would 

### offer some advantages 

### for weight-limited carriage, andfive-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 onfive-axle twinswould enable 

### existing operators

### oftwin 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 capwould be expected to result 

### in a modest 

### shift 

### fromfive-axle48-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 al

### lowed) the preferred 

### new configurationwould 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 con

### figurations used

### by 

## LTL operators. 

### However, replacing existing weight 

### limits on seven-axle triples by those allowed under Bridge Formula 

### Bwould permit increased loading 

### of triples 

### in several Western stateswhere constraints imposed 

### by 105,500 pound 

### GVW 

### limits would be eliminated. 

### Simi

### larly, 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 underthe 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 towhich 

### GVW limits would be raised; and 

### -The likely percentages of shipments originating orter

### minating at locations that could be 

### efficiently served 

### by semis and doubles operating 

### at the 

### new GVW Iimits.



## . Likely responses ofdifferent types oftruck operators (as discussed above).



## . Region-specific distributions 

## ofVMT across 

### trailer types obtained from aspecial tabulation of datafrom the Truck Inventory and Use Survey (TIUS). 155


### All impacts were estimated aschanges from the 1995 base case,

### butthey 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 betaken of the higher weight limits. The analysis assumed there would becontinued liberalization of access provisions for

### twin 28s in Eastern states and no change in current access 

### provisions

## for 48-foot semis. Access 

### restrictions were assumedto reduce usageof

### twin 28s 

### by 50 percent 

### in New 

### England arrd25 percent 

### in the 

### Middle and South 

### Atlantic regions relative tothe usage that would occur in the absence 

### of these restrictions. The analysis assumed that the changes 

### inweight 

### 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 theTIUS data to obtain estimates 

### of the percentages 

### of payloadton-miles currently car

### ried 

### by vehicle configurations (e.9., 

### five-axle semis) 

### thatwould 

### likely be diverted 

### to various alternative 

### ("new") confi gurations. 

### 3. Estimate the 

### distribution 

### ofdivefed ton-miles across operatingweights 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), estimatethe 

### shift 

### in the distribution of ton-miles across operatingweights. 

### 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 andweight regulations on transport demand isthe estimation 

### of diversion

### to or

### from other modes.16 

### For this purpose, the Sydec study used the results 

### of aspecial run

### of the Intermodal Competition 

### Model 

### (ICM), a proprietary

ì5As 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 asmall increase in circuity, VMT of six-axle semis was estimated to increase by l-41 billion. tóAn additional step estimating induced or suppressed demand can be performed. However, because this effect is quite small anddifficult to quantify, it usually is not considered to beworth estimating.156

model developed by the Association ofAmerican Railroads 

### (AAR). This model is described 

### briefly 

### in Section H.1, and some results produced 

### by this model are discussed 

### in Sec

### tion G.1. 

### Forthis 

### run of the 

### ICM, the Sydecstudy team 

### provided 

### AAR with cost specificationsthat 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, thealternatives consisted

### of
asix-axle semi and anappropriate 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 

### reflectthestudy team's estimate of the 

### actual

### ability 

### of the alternative configurations 

### to operate between relevant O/D pairs atthe proposed bridge 

### formula limits. The results of each of the 

### ICM runs included estimates, 

### by commodity group,of:



## . Rail ton-miles diverted totruck;



## . 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 othertraffic retained by the railroads.

Each 

### ICM run also produced 

### a single estimate 

### (not by commodity group) 

### of the relative usage 

### of semis andtwins by diverted 

### traffic. For each 

### policy option, this last estimate
was used to split the scaled 

### ICM estimates of increasedtruck 

### ton-miles between semis and

### twins. The increase 

### in semitrailer ton-miles wasthen distributed across weight brackets for six-axle semis using the samedistribution aswas used

### for freight carried by five-axle semis converting tothisconfigu

### ration; andthe increase 

### in twin-trailer ton-miles was simi

### larly distributed across heavytwin configurations andweight brackets. 

### The distributions 

### of increased 

### ton-miles across 

### weight brackets 

### were then used

## to derive estimates 

### of increased 

### VMT.

# APPENDIX J

INFORMATION NEEDS

Identification ofthe information needs perceived by pub

### lic agencies was an 

### important objective 

### of Phase

## I of this study. 

### Interviews

### with and surveys 

### of public officials were conducted to: (1) 

### identify thefreight 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 andother federal agencies;



## . A survey

### of state DOTs and other stateagencies poten

## tially 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

### ourfindings 

### 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 afourth appendix tothat 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 ontransportation flows of changing patterns 

### in international trade, and, 

### in particular, 

### in changes 

### likely to result from the North American FreeTrade Agreement 

### (NAFTA). The interest 

### in 

### NAFTA is due not 

### onlytothe immediacy 

### of the liberalization 

### of trade rules and cross-border transporta

### tion operations, but also tothe current high rates 

### of growth 

### in the affected trade 

### (12 percent 

### peryear 

### with Canada, 7 percent 

### with Mexico). One person generalized the interest 

### in international trade to include 

### all influences ontransportation 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 spe

### cific 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

### truckflows, numbers 

### of trucks and

### truck drivers, 

### truck

### VMT by time 

### of day, distinctions between domestic and international airfreight and between the land and air portionsof intermodal movements, the true origins anddestinations of 

### rail intermodal movements, andthetrue 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 plan

### ners, 

### so we prepared separatesummaries 

### 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 improvementsto 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 forecaststhat 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 anaICambridge 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), butthatdisclosure 

### limitations would create major gaps

### for state-level analyses andthe 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 interestwas 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 por

### tion 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 alsoclear interest in developing aset

### ofperformance measures 

### for the transportation system, though this would seem to be more asupply issuethan ademand issue. 

### Policy issues 

### of interest 

### to federal 

### officials include the effects of 

### NAFTA; truck size andweight policy (and the relationship between 

### NAFTA and size andweight policy); dereg

### ulation and reregulation; 

### jusfin-time requirements; strikes ; ISTEA regulations; landside accessto ports and airports; 

### rail 

### mergers; 

## rail abandonments;

## and weight-enforcement problems related totruck transport 

### of shipping containers.

J.2 FEDERAL TRANSPORTATION PLANNERS


### Unlike policy analysts, transportation planners are specifically interested intransportation forecasts, quite frequently 

### for far into the future. Usually theforecasts

### of interest represent the demand

### for aspecific 

### facility or setof relatedfacilities. 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 outto 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 betailored tothe special characteristics of the situation. Although many of the COE's past forecasts have beenproduced 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 dataobtainable. Short-term analyses are also usedfor 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 onthe more comprehensive and lesstimely waterborne commerce data. 

### Environmental issues 

### of increasing interest relate

### to analysis 

### of sediment 

### flows, disposal sites

### for dredged material, where fleeting should beallowed, andthe type,volume and routes of hazardous materials movements. Other federal respondents mentioned state and MPO needs 

### for forecasts 

### of access requirements at ports and 

### railfacilities, 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 STATEAGENCIES
Surveys were mailedtothechief 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 surveyform, tabulationsofresponses to closed-ended questions, and summaries 

### of responsesto open-ended questions

are presented inAppendix C of ourfirst Interim Report.2 That appendix also contains summaries

### of follow-up telephone interviews 

### with DOT personnel 

### in selected states. 

### More than 

### halfofall states surveyed indicated that they had 

### major planning responsibilities 

### for truck, 

### rail, and

### air 

### freight (see 

### ExhibitJ.1). Few indicated 

### major planning 

### responsibility 

### for ports and none indicated 

### major planning responsibility for pipeline orwarehousing. Also, many states indicated thatthey had regulatory responsibility 

### for trucks, rail, and airfreight (see Exhibit J.2) and afew states indicated thatthey hadoperating responsibilities (see 

### Exhibit J.3). States 

### were asked 

### to identify the planning and policy issues 

### for which freight transportation forecasts

### would be most valuable totheir agency. The most commonly selected items 

### by the 38 states respondingwere as

### follows:



## . Highway needs analysis (36 states)



## . Truck routes and restrictions (35 states)



## . Highway planning (35 states)



## . Truck size andweight regulations (34 states)



## . Planning of 

### trucVrail intermodal 

### facilities (35 states)



## . Airportplanning (31 states)

2lbid.

### 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 thatthey had 

### little or no experience 

### in freight forecasting. Exceptions were 

### Iowa 

### (which indicated extensive use

### oftraffic and commodity forecasts to support efforts to 

### identify needed transportation 

### improvements),Oregon 

### (which used 

### freight forecasts 

### in developing its statewide transportation plan), andV/ashing

### ton (which usesforecasts 

### in rail abandonment cases and 

### in preparing itsfreight 

### rail plan andairport 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 dataon waterborne commerce



## . Truck counts andtruck weight data Nl Truck 

### E Rail 'ffi

### Air
EH InlandWater @ Maritime (ports) W Pipeline 

### E Warehousing 159

80



## . Air cargo 

### activity by airport



## . Reebie andAssociates Transearch data



## . Census Bureau's Truck Inventory and Use Survey

J.4 METROPOLITAN PLANNING ORGANIZATIONS

100

E-LiLi+ Tt S+-+- Tl¡.l'l-. .-i+L Fôôrtlô+^F¡, --.-^-.iLili+., f^- f--:-1,+

¿.2. vúalv ¡ !É,u¡aüvr J r çùI,v¡¡É¡ur¡¡üJ tvr ¡r v¡5ü! 

### (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'henasked

### 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 shareview 

### it as "somewhat important" to 

### "very important", 

### particularlywith respect to noise/conges

### tion and

### traffic management issues and landside access and 

### facility planning (see 

### Exhibit J.4).

NlTruck 

### E Rait 

### ffi Air 

### ffi InlandWater @l Maritime (ports) 

### ffi Pipeline El Warehousing160


### Exhibit 

### J.3. State 

### DOTs

### with operating responsibility 

### for freight (percent).

The 

### principal sources

### offreight datacompiled or used by 

### individual MPOs include 

### traffic and 

### truck counts, some 

### of which are provided by state DOTs. Those MPOs which rou

### tinely usefreight data rely 

### primarily on data from secondary
sourcesor on datacollected 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. Ofthe 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 oneon air cargo. 

### Two MPOs anticipate increased
use of forecasts 

### in thefuture. 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 anddevelopment. 20 

### N Truck 

## I Rail 

### ffi Air m InlandWater @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 responsibil

### ities

## 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 andtechnical committees; and 

### (b) organizing 

### freight advi

### sory councils 

### or task forces 

### which include 

### local freight interests. 

### Many 

## of the respondent 

### MPOs are seeking 

### ways to 

### improvetheir knowledge and expertise 

## in freight issues either through 

### additional staff, consultant support, 

### or staff training programs. Some expressed a needfor better data and tools relating tofreight 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 I3 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 thatfreight demand forecasting is an important component intheir 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 totime series analysis and econometric 

### modeling. They 

### utilize both in-house data gathered

### from carriersand 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 oCICN 

# 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 ooU) 

## o) 

# tr o c; 

# z

Planning/Policy Function o\ N)The 

### principal sources 

### offreight and

### freighrrelated data

compiled and/or used by the ports include:

Journal of Commerce 

## PIERS 16 In-House Carrier/Shipper

## Data 14 Bureauofthe

## 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 thetwo most frequently mentioned. Others included the \ryEFA Group, BST Associates, andTRADE (formerly TIPS). Thefrequency with which the portsdevelop or contract 

### for freight forecasts rangedfrom quarterly or semi-annually (on

acommercial 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.7AIRPORTS

Coordinate with

State DOT MPO City/County Agencies HighwayTransportation Authorities Advisory Councils Corps of Engineers Railway Commission State Dept. of Commerce Carriers/Shippers



### J.6INLAND RIVER PORTS 

### While atotal 

### of 30 surveys were mailed to those airpons 

### with the grcatestvolume 

### 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 airfreight issues or cargo development. In recent years, several airports have begun to realizethat air

### freight services are or should be an important component oftheir operations. However, planning 

### for airfreight operations remains farbehind thatforpassenger operations. A case in point isthe new international airport at Denver. 

### Air cargo interests were sodissatisfied

### with the location proposed 

### for them atthe 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 atthe new international ai¡port were revised to ensurethatair

### freight operations were more accessible to the surface transportation network. The concept of all-cargo airports has alsogrown in recent years,with 

### AllianceAirport inTexas being the most notable example. Plans

### for a "globaltranspark"-intermodal and 

### industrial complex centered 

### around 

### an

### airport-has also been proposed 

### in the State of North Carolina. Depending on the location of anairport, the issue of night

### time operations 

### and associated 

### aircraft noise 

### is one 

### with which many airports continueto grapple. Because a

### significant share of airfreight 

### operations-particularly thoseofthe 

### 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 tothe survey, most 

### felt thatfreight demandforecasting was importanttovery important 

### with respect 

### to air carrier and all-cargo operations, as 

### well as land use planning and landside access. Only two of the airports indicatedthey develop their own freight forecasts. One indicated useof

### trendline analysis and the other regression analysis to develop theforecasts. For 

### fiveof the 

### six airports, the 

### monthly airline reports

### of pounds enplanedand deplaneda¡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 com

### mercial forecasting service, 

### while another 

### relies 

### on an outside consultant to develop periodic airfreight forecasts. Three

### of the respondents indicated they coordinate planning

### with federal, state, and local government agencies. One coordinates 

### with aprivate 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 developsanytype 

### offreight 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