Freight and Distribution

YRC announced today that it will begin discussions with the International Brotherhood of Teamsters (”IBT”) to seek to modify the terms of the current labor agreement for its employees covered by the National Master Freight Agreement. These discussions will address alternatives to help to reduce the company’s cost structure and preserve operating capital going forward.

Bill Zollars, Chairman, President and CEO of YRC Worldwide, said, “Entering into discussions with the Teamsters is another important step in our overall plan to strengthen our financial position during this difficult economic climate. We have made progress with various stakeholders, including our pension plan trustees and our bank lending group, to modify agreements, and we are grateful to the Teamsters for their willingness to consider further adjustments to our contracts to help reduce our cost structure and enable us to be competitive with others in our industry.”

YRC Worldwide recently announced an agreement with Central States, Southeast and Southwest Areas Pension Fund (”Central States”), the largest of the company’s IBT multi-employer defined benefit pension funds, to provide certain of the company’s real estate as collateral in lieu of pension contribution payments during the second quarter. The company also announced an amendment to its bank agreement, which provides for the immediate release of escrow funds generated from the company’s prior real estate transactions to pay down the revolving credit facility without reducing the company’s borrowing availability under the facility.

YRC Worldwide Inc., a Fortune 500 company and one of the largest transportation service providers in the world, is the holding company for a portfolio of successful brands including YRC, YRC Reimer, YRC Logistics, New Penn, Holland, Reddaway and YRC Glen Moore. Building on the strength of its heritage brands, Yellow Transportation or Yellow Trucking and Roadway Express, the enterprise provides global transportation services, transportation management solutions and logistics management. The portfolio of brands represents a comprehensive array of services for the shipment of industrial, commercial and retail goods domestically and internationally. Headquartered in Overland Park, Kansas, YRC Worldwide employs approximately 49,000 people.

On the U.S. side, the Ambassador Bridge is located beside I-75 near its intersection with the eastern terminus of I-96. The bridge crosses only 2 miles south of downtown Detroit. Since space is at a premium as a result of the developed urban and industrial setting, a discounted freight truck approaching the US side must make an abrupt, tight 180 degree turn to enter primary on the U.S. side. Freight trucks on the Canadian side have more room for a straightforward approach. The bridge entrance and exit on the Canadian side is in a less industrial setting next to the University of Windsor. Truck traffic exits onto Highway 3, which after 5-1/2 miles intersects Highway 401, a major route that heads northeast across Ontario.

The bridge and its collateral facilities are privately owned and operated by the entity known as the Ambassador Bridge. The mission of the Ambassador Bridge is to operate and maintain the bridge and collect tolls on both sides of the crossing. The Ambassador Bridge’s US owner is the Detroit International Bridge Company and its Canadian subsidiary is The Canadian Transit Company. Ambassador Bridge owns the facilities that house Canada Customs and Immigration while GSA owns the US Customs facility. Since Ambassador Bridge owns and operates the property that the tollbooths are on, the data collectors who were located by the toll collection booth in either country had to have permission from them, which was verbal.

The US and Canadian Customs mission is to protect their border. They operate the facilities and control the property where their Customs facilities are located. Data collectors who were operating beside the primary Customs checkpoint in either country had to have permission to be on the property operated by the Customs organization of that country. So even though that data collector was only a very short distance from the collector at the tollbooth, the approval to operate at that spot came from a different organization.

Model validation involves testing the model’s capability to predict current travel demand so that it can be used effectively to predict future travel demand. Freight shipping travel models need to be able to replicate observed conditions within a reasonable range before they can be used to produce future year forecasts. As metropolitan areas continue to refine and improve their travel demand forecasting processes, the credibility of the process with decision makers will depend largely on the ability of analysts to properly validate the procedures and models used including discount freight shipping. The travel demand models have become more complex, resulting in complex procedures needed to validate them. Often there are tradeoffs between increasing confidence in the level of accuracy of the models and the cost of data collection and effort required to validate them. Tests used to evaluate the reliability of models can range from a simple assessment of the reasonableness of model outputs to sophisticated statistical techniques.

Key data components include annual productions by economic sector, employment by industry sectors, and in?migration and payroll by the economic sector. Besides economic production and industry employment data, this data also includes these sectors.
Production Allocations and Interactions: The production allocations and interactions module determines the distribution of production activity among zones and the consumption of space by these production activities in each zone. The module also reflects the flows of goods and services and labor from production locations to consumption locations, as well as the exchange prices for goods and services, labor, and space each year.
Household Allocations: Household allocations to zones reflect the same distributions as the allocations from the previous year. The labor flows originating from these households are allocated to the production locations based on the production allocations to zones determined from the production allocations and interactions data. Similarly, distribution of freight companies demand associated with household consumption activity is modeled by allocating the flows of commodities consumed by the households to zones based on zonal production allocations.
Land Development: The land development data estimates the year-to-year changes in available space in each zone in the region. The primary task of the land development data is to adjust the quantity of space over time in the region in response to changes in price. Other data in the model determine a price for each category of space in each zone using a highly dis-aggregate process, based on the fixed supply of space available in each zone for that particular year. The data uses the zoning patterns and does not forecast how the political process can change zoning patterns. This data does not include freight transportation.

An intermodal terminal or port can be defined as a location for the transfer of freight from one transport mode to another.  Such modes as between a ship transferring freight to Yellow Trucking or a truckload carrier transferring freight to an airline. The coordination of resources to achieve intermodal efficiency is a challenging task that involves government, the private sector, and various other groups. Intermodal terminals serve as principal interchange points for both international and domestic freight movements.

The data collection efforts at intermodal terminals are always a challenge owing to the enormous time and costs associated. In addition, these data are specific to each type of intermodal terminal and cannot be transferred or borrowed. Specific models also are built based on the capacity and volume of traffic being handled at these facilities.

The modeling framework of economic activity models (integrated modeling of the economy, land use, and freight demand) is often referred to as a spatial input-output model. A spatial I-O model involves an economic component that defines household and economic activity and industry economic relationships in the region. A land use component that distributes household and economic activity across zones and a spatial transportation component that defines the links and nodes of the network connecting the zones, and finally computes transportation flows on the network. All of these components are integrated together for freight flow forecasting. Next, shipping quote and freight quote data.

This section provides a more in?depth look at the two essential components of economic activity models, namely the economic and land use model and the freight demand model. The economic land use component of the model generates socioeconomic forecasts at the zonal level of detail, based on considerations of the structure of the economy and the locations of industrial and household activities in the region in the future. These socioeconomic forecasts along with industrial activity location and economic interrelationships information are used interactively with the freight travel demand model to develop freight trip generation and distribution estimates. The travel demand model component then performs the mode split and network assignment steps to predict freight flows on the network by each mode of transportation.

Changes in land use may have a negative impact on trucking freight transportation, especially with regard to facilities in densely developed urban areas. The economic land use model should be able to replicate the observed shift of freight facilities to areas distant from urban centers which have cheaper land in the large parcels often required by modern logistical and freight centers.

Next, freight quote data

Logistic nodes are used in supply chain and logistic chain models that use economic input-output characteristics to calculate supply and demand for each economic sector with an assignment of goods to logistics families to determine the spatial patterns of supply and demand. The logistic nodes are used as means to distribute or disseminate the external movements to internal zones. These nodes are places such as major goods yards, multimodal terminals, railway stations, and distribution centers where trip chaining of long-distance flows occurs.

The freight forecasting modeling process involves the representation and modeling of the long-distance logistics system in the Transport Logistics Node model. This model is only applied on the long-distance flows. These are defined as flows from the internal area (for example the greater southern California area) to the external area (the remainder of the United States as well as entry points to/from Mexico and Canada) and flows from the external area to the internal area. Data was collected through a shipper survey conducted for 131 locations in Southern California combined with rail operator data obtained at six intermodal terminals.

The following are some of the critical issues that need to be addressed:

• freight truck discounts
• discount freight rates

The  trucking companies truck tables developed from the data were further processed to evaluate the origin and destination of the commodities with respect to the Puget Sound region. These tables were compared to total volumes of truck trips at the external stations and to total internal volumes from the trip generation model. The truck trips for external trips (both internal-external and through trips) compared favorably to the total truck volumes at external stations for heavy trucks. The internal truck trips represent 32% of the total internal heavy truck trips estimated in the trip generation model, so these were used to estimate trip rates for manufacturing and wholesale trade.

The data identifies freight shipping rates for the origin and destination of commodity flows for 30 geographic markets. These regions were associated with appropriate external stations and internal Puget Sound counties to disaggregate these data into traffic analysis zones. Modifications to the original dataset were made to eliminate those commodities that would not likely travel through Puget Sound. The data provided a direct calculation of external (through) trips. These through trips were subtracted from the total heavy truck counts to provide an estimate of internal-external and external-internal trucks at each station. It was assumed that all commodities were moving on heavy trucks. The internal-external and external-internal trucks were distributed to internal zones using the same allocation by industry as the internal truck trips.

The truck model converted commodity flows into truck trips using data from a combination of surveys and data from the 2002 Census Bureau. First, the tons were allocated to the three truck classes in the model (light-heavy duty freight trucks, medium-heavy duty freight trucks, and heavy-heavy duty freight trucks). Next, the tons in each of the truck classes were converted to truck trips using the payload data from the intercept surveys. Weigh-in-motion data were used to convert annual truck trips to daily truck trips. This disaggregation process converted the annual truck tons in the commodity flow database into a daily zone-level truck trip table.

The internal component of the truck model is being updated based on new truck travel surveys. This component will estimate truck travel for trips where both the origin and the destination are within one of the six counties. The newer internal model will be a three-step shipping rates freight truck model just like the current model.

State-of-the-art metropolitan freight truck discounts models are hybrids that blend commodity flow modeling techniques with freight truck modeling techniques. Commodity flow databases tend to be relatively accurate for inter-county flows, but undercount intra-county flows because commodity flow databases rely partly on economic input-output data that ultimately are based on financial transactions between producers and consumers of goods. However, in an urban area, many freight shipping companies truck moves are not easily traced to such transactions. Moves from warehouses and distribution centers, repositioning of fleets, drayage moves, parcel delivery, and the like are generally short-distance trips in which there may not be an economic exchange of the goods from one party to another. To compensate for the lack of inclusion of the shorter distance trips in commodity flow data, and to account for types of trucks that do not carry freight, local truck trips are generated based on local employment and economic factors using trip generation rates. These trips are usually generated at the zone level and trip distribution uses methods such as gravity models. The trip rates are calibrated so that the truck traffic volumes that are generated from the combined commodity flow and locally generated truck trips match those from available truck counts. Several terms are used to refer to these two trip types, including commodity-flow trips versus locally generated trips, external versus internal truck trips and long-haul versus local truck trips. Taking advantage of the relative strength of the commodity long-haul approach and the truck short-haul approach within the same model has been called a “hybrid approach.” The two modeling frameworks – freight truck models and commodity-flow models – are described briefly in the following sections. These two models form the basis for the freight/truck hybrid forecasting procedures.

Freight traffic flow data has limitations with respect to trucks. The primary coverage of trucking companies traffic is limited for non-manufactured products. Supplemental purchases can provide for agricultural and mining resource extraction shipments from the source to a processing plant that are not ordinarily covered in commodity flow surveys.
Traffic movements originating in warehouses or distribution centers or drayage movements of intermodal rail or air freight are shown as STCC 5010. These are by definition truck movements. Movements to warehousing and distribution centers may be by other STCC codes and by any mode. Details on the types of items being moved are not available as are shipping freight quotes.
The inland or surface movements of import and export traffic volumes to locations outside of North America are included in the data. However, the flow patterns of this freight are based on the movement patterns of domestically sourced goods in the same market areas and are not the actual movements of the import/export freight.

Freight carried by trucks, based on the definitions used by the principal agencies collecting data, also typically excludes shipments to or from retail (excluding mail-order and warehousing), offices, service establishments, and residences. These local freight or goods deliveries are significantly different from those freight shipments that are included in terms of the distances traveled, the type of trucks used, the times of movement, and the routing of the shipment, but their exclusion does not detract from the larger freight-related issues.

The Tennessee Freight Model applied growth rates for industries available from economic development agencies. It applied those factors differently to industries producing freight than to industries consuming freight. Freight shipping rates are not a factor. The relationship between commodities and producing industries. In almost every case 100% of the growth in the outbound shipment of commodities is related to the industry producing that commodity. The relationship of the inbound freight (consumption) shipment of commodities to the employment industry groups used in the model. These will be quite different from the industry producing that commodity. For example, 58% of the agricultural shipments are consumed by manufacturing, 19 percent are consumed by populations, and 14% are consumed by the agricultural industry, with the balance in service and government. The growth in the outbound shipment of commodities is the application of the growth in each of these industries.

This step of the freight carrier modeling process estimates the average total freight trips by mode that would be generated by the planned facility for a specific time period (daily, annual, etc.). The total trips generated by the facility include both production, originating from the facility, and attraction, destined to the facility, trips.

The most common methods used for facility trip generation include trip generation rates, regression equations, and surveys. Using trip generation rates is the simplest approach for trip generation, in which estimates of number of trips per employee are applied to the target facility to estimate the total trips generated. Trip generation rates also can vary based on truck types and the type of facility (land use). The trip generation rates used in this approach can be derived from previous surveys of freight flows associated with similar facilities or from standard sources providing average trip generation rates for facilities, based on facility and truck types.

The use of regression equations for trip generation offers the ability to predict the total trips generated as a function of more than one facility variable, which makes this approach potentially more robust and reliable compared to the use of trip generation rates. For example, a regression equation predicting total daily freight trips as a function of land use category, number of employees, and building/floor area. However, caution should be maintained when developing and using regression equations for trip generation, as equations with statistical inconsistencies will not result in reliable estimates.

Conducting surveys is the most time- and cost-intensive approach for trip generation, but it can provide the most accurate results, compared to trip generation rates and regression equations. This approach is useful in the case of special trip generators such as intermodal terminals, in which trip generation estimates are derived through direct contacts with a limited number of firms (facility operators and users – truck companies, shippers, etc.). This approach is particularly effective if the planning agency has been building contacts with the freight community over a longer period of time.

Freight shipping companies

For statewide freight models, data are needed to develop and specify the equation used in the various steps, and forecast adapt is needed in the same format to create freight flow forecasts. These tables tend to have limitations that must be overcome in using them to survey as freight surveys for model development. The CFS is publicly available only for 114 zones nationally, but the number of zones increases the purchase price. The challenge in the use of both models is to develop zone structures that are detailed within the model study area, the state, and increasing less detailed at distances from the state model area. The commodity table typically has what is referred to as two-digit level of detail. Employment data are needed at an industry detail matching this freight commodity structure. Even the 40-50 commodities available provide data management and computational challenges and commodities carried forward are typically those that are the largest and most important to the study area. The associated employment must be available for those important commodities but may be aggregated to less detail matching the aggregated commodities. For example, printing may be included with all non-durable manufactured goods while food products would be retained as a separate category.

Freight carrier

These commodity-flow surveys also provide information needed to calibrate the trip distribution and mode split steps. Commodity flows will typically need to be converted into units of daily vehicles because this more easily integrates with passenger forecasts and other transportation design, and operations tasks are typically based on daily flows. Data are needed to develop factors that can be used to convert from annual tons to daily trucks. The model needs to be validated to observed counts. This validation data, on highways, is observational, such as truck classification counts and typically will have no information on the commodities being carried. Since observational counts also include no information on truck purpose, those counts probably include trucks carrying local delivery of local freight or trucks used in construction, service, and utility trucks, none of which are included in the freight commodity model. Conversion from annual flows to daily modal vehicle flows is needed only for those modes that will be used in assignment.

The general notion of building a trip table involves assuming that productions equal attractions. Depending on the availability of truck companies travel survey data, trip rates for a given sector or land use are either considered the same for production and attraction or they are estimated separately at each trip end. If the trip rates are assumed to be the same at both ends, then typically these are land use-based trip rates.

If data permits estimating two different rates for production and attraction, then these may be either employment or land use based trip rates. Meaning, the employment at that particular land use will drive the productions and/or attractions for any given sector. For example, “retail employment” in a TAZ can produce and attract trips that belong to the “mail/parcel” sector, if the supported by the data. If there are 200 “mail/parcel” expanded trips that are produced from a “retail” store, and if there are 300 “mail/parcel” expanded trips that are attracted to a “retail” store, then the production rate will be (200 trips/retail employee) and the attraction rate will be (300 trips/retail employee). These rates also can be estimated based on regression techniques where the dependent variables if the number of truck trips for a given sector and the independent variables are different types of employment. The coefficients associated with each employment variable are the trip rates. In other words, every sector (or trip purpose) will have a production rate and attraction rate for every type of land use (or employment) where trucks in that sector make stops at. Discounted freight shipping traffic will have been included.

An intermodal terminal can be defined as a location for the transfer of freight from one transport mode to another such as between water and road (ports), road and rail (rail yards), or air and road (airports). The coordination of resources to achieve intermodal efficiency is a challenging task that involves government, the private sector, and various interest groups. Intermodal terminals, which include seaports, airports, and rail terminals, serve as principal interchange points for both international and domestic freight movements.

The data collection efforts at intermodal terminals are always a challenge owing to the enormous time and costs associated. In addition, these data are specific to each type of intermodal terminal and cannot be transferred or borrowed. Specific models also are built based on the capacity and volume of traffic being handled at these facilities. The Southern California Association of Governments (SCAG) HDT model and Los Angeles Metropolitan Transportation Authority) LAMTA CubeCargo model are perhaps the only two models that capture the freight carriers truck traffic coming out of and going into each of these three intermodal facilities in the region at the TAZ level.

The most important data that cannot be transferred or borrowed are the classification counts. Every model update includes the collection of this data. These are used to calibrate and validate the traffic assignment process that includes both passenger cars and trucks. Some agencies have a continuous traffic count program on key facilities such as freeways and expressways that are used in regular time intervals to update regional travel models. The level of detail of truck counts by various truck types or classes largely depends upon the truck model structure. Most count programs collect axle-based truck classification counts as these are easily captured by manual and machine counters. Agencies that use truck models based on GVW ratings convert the axle-based truck counts to appropriate GVW classes based on internally developed algorithms. The count locations also are important in the validation process of a truck model. These are usually collected on all the major facilities such freeways, expressways, and arterials. These also are collected at various points on a screenline and many screenlines are defined upfront of the count program. In addition to counts, other observed data that is necessary are truck speeds or travel times on key routes. Data still needed on shipping rates and freight shipping quote.

The trucking companies rates for transporting goods is reflected by a number of factors besides basic transport costs, such as the class of the commodity. Non-breakable and non-perishable items, like truck engines, are carried more cheaply than ping pong balls. The more careful the handling required, the more expensive is the freight rate. Sophisticated manufactured goods can bear high freight rates because of their greater value. Distance and weight are also two key factor. Many freight rates are scaled; that is, it is cheaper per hundred lbs for a 5000 lns shipment than it is for a 200 lbs shipment.

The total number of axles on the trucks are normally categorized into four axle categories – two axles with four tires, two axles with six tires, three axles, and four or more axles. This information on vehicles can be obtained by visual identification or manual counts, or the use of axle sensor-based counters that are often used to collect accurate truck counts. The number and spacing of axles is used to classify trucks into FHWA’s 13-category classification scheme. Most of the vehicle classification count studies across the country classify freight carrier trucks into these 13 categories, as listed below:

• Class 1:  Motorcycles (Optional) – All 2 or 3-wheeled motorized vehicles. Typical vehicles in this category have saddle type seats and are steered by handlebars rather than steering wheels. This category includes motorcycles, motor scooters, mopeds, motor-powered bicycles, and three-wheel motorcycles. This vehicle type may be reported at the option of the state.
• Class 2:  Passenger Cars – All sedans, coupes, and station wagons manufactured primarily for the purpose of carrying passengers and including those passenger cars pulling recreational or other light trailers.
• Class 3:  Other Two-Axle, Four-Tire Single Unit Vehicles – All two-axle, four-tire vehicles, excluding passenger cars. Included in this classification are pickups, panels, vans, and other vehicles such as campers, motor homes, ambulances, hearses, carryalls, and minibuses. Other two-axle, four-tire single-unit vehicles pulling recreational or other light trailers are included in this classification. Because automatic vehicle classifiers have difficulty distinguishing Class 3 from Class 2, these two classes may be combined into Class 2.
• Class 4:  Buses – All vehicles manufactured as traditional passenger-carrying buses with two axles and six tires or three or more axles. This category includes only traditional buses (including school buses) functioning as passenger-carrying vehicles. Modified buses should be considered to be a truck and should be appropriately classified.
• Class 5:  Two-Axle, Six-Tire, Single-Unit Trucks – All vehicles on a single frame, including trucks, camping and recreational vehicles, motor homes, etc., with two axles and dual rear wheels.
• Class 6:  Three-Axle Single-Unit Trucks – All vehicles on a single frame, including trucks, camping and recreational vehicles, motor homes, etc., with three axles.
• Class 7:  Four-or-More-Axle Single-Unit Trucks – All trucks on a single frame with four or more axles.
• Class 8:  Four-or-Fewer-Axle Single-Trailer Trucks – All vehicles with four or fewer axles consisting of two units, one of which is a tractor or straight truck power unit, used by freight shipping companies.
• Class 9:  Five-Axle Single-Trailer Trucks – All five-axle vehicles consisting of two units, one of which is a tractor or straight truck power unit.
• Class 10:  Six-or-More-Axle Single-Trailer Trucks – All vehicles with six or more axles consisting of two units, one of which is a tractor or straight truck power unit.
• Class 11:  Five-or-Fewer-Axle Multitrailer Trucks – All vehicles with five or fewer axles consisting of three or more units, one of which is a tractor or straight truck power unit.
• Class 12:  Six-Axle Multitrailer Trucks – All six-axle vehicles consisting of three or more units, one of which is a tractor or straight truck power unit.
• Class 13:  Seven-or-More-Axle Multitrailer Trucks – All vehicles with seven or more axles consisting of three or more units, one of which is a tractor or straight truck power unit.

These methods are the most comprehensive as they examine the characteristics of each individual shipment and the available modes. The most common type of choice method is the discrete choice logit model. This formulation is very similar to the passenger mode choice modeling, but the variables and data sets used to estimate the parameters are very different. The logit discrete choice model shows the choices for individual shipments as a function of the utility that each mode provides to the shipper. Utility can be a function of any of the factors mentioned earlier in this section.

The logit model actually calculates the probability that each shipment will use a particular mode. Summing the probabilities across all of the shipments provides the overall mode share. Each modal alternative has a utility to the shipper that has a systematic component related to the factors we have described earlier and a random component that has to do with things like personal relationships. The coefficients in the utility function measure the relative importance of each factor in determining mode choice. The greater the utility that any alternative has, the higher the probability that this alternative will be selected.

Logit choice models are the most complete with respect to modeling all of the factors that affect mode choice. Thus, they can be applied to a wide range of policy and investment studies. However, they are complex to build and are very data intensive. Most of the data needed require the use of complex performance or simulation models. The truck surveys are helpful for estimating the choice parameters, but these surveys are expensive and time-consuming to conduct.

Freight Carriers News is news on freight shipping

One alternative to the use of growth factor methods for forecasting freight travel demand is regression analysis. While the historical growth or time-series methods discussed in Section 3.2 also involve regression of observations against time periods, regression analysis as it is discussed here involves identifying one or more independent variables (the explanatory variables) which are believed to influence or determine the value of the dependent variable (the variable to be explained), and then calculating a set of parameters which characterize the relationship between the independent and dependent variables. For freight planning purposes, the dependent variable normally would be some measure of freight activity and the independent variables usually would include one or more measures of economic activity (e.g., employment, population, income). For forecasting purposes, forecasts must be available for all independent variables. These forecasts may be obtained from exogenous sources or from other regression equations (provided that the system of equations is not circular), or they may be developed by the forecaster using other appropriate techniques.

For forecasting purposes, regressions normally use historic time-series data (an alternative is cross-section data) obtained for both the dependent and independent variables over the course of several time periods (e.g., years). Regression techniques are applied to the historic data to estimate a relationship between the independent variables and the dependent variable. This relationship is applied to forecasts of the independent variables for one or more future time periods to produce forecasts of the dependent variable for the corresponding time periods.

It should be recognized that the economic forecast described above, to some extent, has been developed by regression and calibration to observed data.  The use of regression of observed freight flows to economic data should be used with caution as an alternative to the economic forecast described above which also may consider many factors that cannot be considered in a simple regression.

Freight Transport Report provides info on freight transportation

The growth factor methods presented above produce just a single forecast of freight demand. Planning decisions can then be made on the basis of this forecast. However, planners are cautioned that the forecast is likely not to be completely accurate either because some of the assumptions (e.g., those relating to economic growth) prove to be inaccurate, or because of deficiencies in the procedure itself. Because no forecast can be guaranteed to be perfectly accurate, effective planning requires that planning decisions be reasonably tolerant of inaccuracies in the forecast. The conventional approach to analyzing the effects of alternative futures is to subject a forecast to some form of sensitivity analysis.

The development of any forecast requires a number of assumptions to be made, either explicitly or implicitly. Some of the types of assumptions that may be incorporated into forecasts of demand for a transportation facility relate to:

• Economic growth – both nationally and locally;
• Growth in the economic sectors that generate significant volumes of freight handled by the facility;
• Transport requirements of these sectors;
• Modal choice;
• Facility usage per unit of freight volume;
• The availability and competitiveness of alternative facilities;
• Value per ton of output; and
• Output per employee (if employment is used as an indicator variable).

Sensitivity analysis consists of varying one or more of these assumptions in order to produce alternative forecasts. The most common alternative assumptions to be considered are those related to economic growth; and, indeed, economic forecasters (including BLS) frequently provide high and low forecasts of growth in addition to a medium (or most likely) forecast. These alternative forecasts of economic growth can be used to generate alternative forecasts of transport demand, and additional alternative forecasts of exogenous variables (e.g., trade) can be used to produce an even larger set of forecasts of transport demand (e.g., high growth, high trade; high growth, low trade; etc.). However, simply varying these exogenous forecasts generally will not produce a set of transport-demand forecasts that represents the full range of demand that might exist in future years of interest. To produce a better understanding of the range of demand that might exist in the future, a more thorough sensitivity analysis should be conducted.

One approach to conducting a thorough sensitivity analysis consists of reviewing each of the assumptions explicit or implicit in the analysis and, for each assumption, generating a pair of reasonably likely alternative assumptions, one that would increase the forecast of demand and one that would decrease it. A high forecast of demand can then be generated by using all the alternative assumptions that would tend to increase the forecast (or at least all those that are logically compatible with each other); and a low forecast can be generated by using all the alternative assumptions that would tend to decrease the forecast. These high and low forecasts should provide planners with appropriate information about the range of transport demand that could exist in the future. Planning decisions can then be made that are designed to produce acceptable results for any changes in transport demand within the forecast range.

A somewhat more systematic type of sensitivity analysis consists of making small changes in the analytic assumption, one at a time, and determining the effect of each change on forecast demand. The results of this effort are a set of estimates of the sensitivity of the forecast to each of the assumptions. This type of sensitivity analysis can provide more insight into the relationships between the various analytic assumptions and the forecasts produced. However, this approach requires a greater expenditure of resources. Furthermore, the most important sensitivity results – high and low forecasts of demand – can be generated using either approach, though these forecasts will be affected by the alternative analytic assumptions used to generate them and the care with which the high and low forecasts are then generated.

Freight Carrier News, news on freight rates

The economic forecast should be applicable for the area being served by the freight facility. There are several sources which can be used by analysts at state DOTs, MPOs, and other planning agencies to obtain estimates of growth in economic activity (by geographic area and industry or commodity type). The availability of data specific to the geographic areas and industries being considered may, however, be limited and compromises may have to be made.

Many states fund research groups that monitor the state’s economy and produce forecasts of changes in the economy. For example, the Center for the Continuing Study of the California Economy develops 10 year forecasts of the value of California products by the NAICS code. [NAICS is the North American Industrial Classification System, a hierarchical coding system for industries.] Similarly, the Texas Comptroller of Public Accounts develops 10 year forecasts of population for 10 sub-state regions and 10 year forecasts of output and employment for 14 industries.

At 2.5-year intervals, the Bureau of Labor Statistics (BLS) publishes 10 year forecasts of output and employment for 242 sectors (generally corresponding to three and four digit NAICS industries).

In addition to the state and Federal agencies, short- and long-term economic forecasts also are available from several private sources. The private firms use government and industry data to develop their own models and analysis. Among the best known private sources are Global Insight (formerly DRI-WEFA) and Woods and Poole.

Global Insight provides national, regional, state, Metropolitan Statistical Area and county-level macroeconomic forecasts on a contract or subscription basis. Variables forecasts include gross domestic product, employment, imports, exports, and interest rates. Their United States county forecasts cover a 30-year period and contain annual data. They are available following completion of our long-term U.S. state and MSA forecasts on a semiannual basis with forecasts of more than 30 concepts, including: income and wages; employment for 11 major industry categories; population by age cohorts; households by age cohorts. The United States county forecasts are updated semiannually.

Woods and Poole provides more than 900 economic and demographic variables for every state, region, county, and metropolitan area in the United States for every year from 1970 to 2030. This comprehensive database is updated annually and includes detailed population data by age, sex, and race; employment and earnings by major industry; personal income by source of income; retail sales by kind of business; and data on the number of households, their size, and their income. All of these variables are projected for each year through 2030.

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Perhaps the simplest and most direct method to forecast future freight demand is to factor existing freight demand. This section provides simple methods that can be used to forecast the changes in freight demand due to changes in the level of economic activity or other related factors. The procedure involves applying growth factors to baseline freight traffic data or economic variables in order to project the future freight travel demands. The growth factor approach is classified into two types – the more commonly used method of forecasting future activity based on historical traffic trends, and the less commonly used method based on forecasts of economic activity. The first approach involves the direct application of a growth factor, calculated based upon historical traffic information, to the baseline traffic data. The second approach recognizes that demand for freight transportation is derived from underlying economic activities (e.g., employment, population, income, etc.). In this approach, forecasts of changes in economic variables are used to estimate the corresponding changes in freight traffic. A simple example is provided at the end of the section to illustrate and differentiate the two approaches.

Growth factors are commonly used by state DOT, MPO, and other planning agencies to establish rough estimates of statewide or regional growth for a variety of types of demand and are certainly applicable to establishing the freight traffic for the freight component of a transportation plan, program, or project design. At the local level, these methods might be used to project growth in freight traffic in a given corridor or the level of activity at an inter-modal facility or port.  This section also briefly describes a more elaborate alternative approach for freight transportation demand forecasting using simple statistical techniques.

The use of growth factors is a simple, inexpensive way to forecast freight, whether based on historical trends or based on historical relationships to economic data, but this method assumes that all of the relationships that are part of that history will continue during the forecast period. It is not well suited for situations that involve dramatic new changes in activity, such as the introduction of a new freight facility offering freight or new developments in shipping or receiving freight. It is most suitable for analyzing incremental changes in freight activity.

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Freight traffic can be represented in many different ways, depending on the mode, type of vehicle/equipment, and commodity. A common representation is in terms of the number of vehicles (for example, number of trucks and carloads, for trucking and rail carload, respectively). Intermodal freight traffic is typically measured in terms of 20-foot equivalent units (TEU), where one TEU represents a standard 20-foot container, while commodity-based representation of freight traffic involves measuring the total weight (tonnage) or value (dollars) of shipments for each commodity group.

Measures of freight traffic flows are important in freight demand analysis for a host of applications such as congestion and safety impact analyses. For example, information on the number of trucks on the network is essential for integrating truck flows with autos on shared-use networks, to understand congestion impacts. Freight traffic flows also are key inputs for safety impact analysis, which are critical in the overall freight planning process for highway and rail modes. In the case of highways, the number of trucks on the network and their fractions relative to total traffic are important parameters to understand interactions between truck and auto traffic, and how they impact safety. Forecasting safety implications associated with rail traffic is particularly difficult, because of the absence of integrated network models, as well as limitations in the capability of government agencies to estimate accurate rail forecasts on private rail networks. Typical approach involves developing general rail traffic growth rates or relying on specific flow data from railroads to analyze rail/passenger conflicts.

Specific applications of freight traffic flow information in freight forecasting include trend analyses and trip generation estimation. Historic measures of freight traffic flows are often used for estimating growth rates based on a trend analysis approach to freight forecasting. Truck trips also are used for facility-level freight forecasting by developing trip generation rates for truck trips as a function of facility characteristics such as employment and land area.

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The air freight system is typically characterized by low weight, small volume, high-value cargo. Consequently, air cargo constitutes a small fraction of total freight tonnage but a higher fraction of total value of freight in domestic and international trade. Air cargo, due to its high value, also has high travel-time sensitivities, implying that slight changes in transit times can have significant cost impacts for air cargo shippers.

Operationally, air freight transportation tends to concentrate in larger metro area hubs. However, it also involves freight moving through some regional freight-only airports. The analysis of hub activity in air freight transportation is important for the development of air cargo forecasts in metro areas. Hub activity also is an important consideration in land side traffic impact modeling, since it generates significant truck trips in metro areas.

Air cargo operations can be divided into air cargo freighters, integrated carriers (like FedEx), and cargo shipments in the belly of scheduled commercial carriers on passenger routes. These operations have distinct routing characteristics and time-of-day patterns, and also may be different in their underlying logistics frameworks.

Other aviation system elements useful for the analysis of air cargo flows include air-cargo terminals and sort facilities. Sort facilities may be located at off-airport sites, which generate truck trips, and also impact truck traffic distributions. Forecasting truck moves to and from these facilities is thus an important component of local freight planning.

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The two main operational types of marine freight transportation include inland and ocean shipping. These two operations not only involve different infrastructure (ocean versus inland waterway ports/terminals), and types of equipment (vessels, barges, terminal equipment, etc.), but also are unique in the types of commodities carried, shipment sizes, freight logistics/supply chains, and trading partners involved. Recent trends towards short-sea shipping services for domestic transportation between coastal cities on the west and east coasts of the United States indicate the importance of this form of ocean transportation in meeting growing freight demand.

The main types of marine transportation services include bulk, break-bulk, container, and roll-on/roll-off, depending on the type of commodity carried. Each of these services is considered separately in freight demand analysis due to the need for distinct representation of commodity flows (tonnages, TEUs [Twenty-Foot Equivalent Unit, a standard measure of container volume], number of trucks, etc.), as well as in the analysis of land side impacts of marine freight flows (for example, land side traffic impacts of bulk transport will be different compared to containerized transport because of differences in mode choices, as well as the size of shipments). Segregations based on the type of service also are pertinent for marine freight forecasting, since each service market is expected to have different growth trends in the future (for example, containerized cargo has been the fastest growing group in marine transport).

Vessel size is another important consideration in the analysis of marine freight demand. Vessel sizes have an impact on the port of call as well as land side traffic flows, and also are key inputs for the analysis of environmental impacts (such as emissions) associated with marine transportation. Other marine transportation system elements, including terminals, container yards, wharves, gates, and land side access routes, play a critical role in the marine freight transportation system and are useful elements to be considered in the freight modeling and forecasting process.

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For YRC Worldwide, formally Yellow Transportation, lenders have agreed to a less stringent credit agreement that eliminates a second-quarter earnings requirement.

The Overland Park, KS based trucking giant said Friday that it had finalized an amendment with lenders for its credit facilities that removes the minimum $45 million earnings before interest, taxes, depreciation and amortization (EBITDA) covenant for the second quarter. The change doesn’t affect requirements for minimum liquidity of cash and cash equivalents, restricted cash and availability under the credit facilities.

“It is still too early in the second quarter to precisely project our earnings results,” Chairman and CEO Bill Zollars said in a Friday release. “Although volumes that were temporarily diverted have begun to return, it has not been at the level and speed that we initially expected, and as a result, we proactively worked through an amendment with our banks to remove any EBITDA targets in the second quarter.”

YRC volumes have taken a hit from a long freight recession, shifts created by the integration of subsidiaries and customers diverting freight because of concerns about YRC’s financial stability.

YRC Worldwide said Wednesday that it has registered with the Securities and Exchange Commission for possible offerings of as much as $200 million in common stock, preferred stock, warrants or a combination of the three.

Overland Park-based YRC, formally Yellow Transportation, said in the filing that it has no transactions planned but that it “believes it is advisable to have an effective shelf registration statement on file with the SEC to be able to take advantage of opportunities as they may arise.”

The filing follows approval Friday by YRC’s lenders of a less stringent credit agreement that eliminates a second-quarter earnings requirement. Also on Friday, The Wall Street Journal reported that YRC plans to seek $1 billion in federal bailout money for pension obligations. YRC officials wouldn’t comment on the report.