Trucking Freight

OVERLAND PARK, Kan., June 18 /PRNewswire-FirstCall/ — YRC Worldwide Inc. (Nasdaq: YRCW) today clarified that the amendment it finalized on June 17, 2009 to its revolving credit facility with its lenders has the same terms in regards to total liquidity and capacity under the facility that existed prior to yesterday’s amendment. The new amendment does give the company immediate access to the escrow funds of $73 million by means of revolver capacity that can be borrowed at any time without approval from the lenders so long as the company’s cash is below $150 million. The $150 million is a new maximum of cash and cash equivalents that was mutually agreed to by the company and the lender group and set well above the company’s average daily cash usage. The company’s total liquidity includes its cash balance in addition to the availability under its credit facilities, which in total was $242 million at May 31, 2009.

“Yesterday’s amendment reflects the continued support of our lender group as we further implement our strategic actions both operationally and financially,” said Tim Wicks, Executive Vice President and CFO of YRC Worldwide. “We now have immediate access to the escrow funds, which is a month before the original agreement, and there is not an immediate reduction to our capacity.”

The company did not pay any fees to the lender group associated with this amendment.

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 and Roadway, 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.

Data concerning the availability of land, industrial land use patterns, and the rules and regulations governing the development of land uses in the future are critical inputs that are used by the economic and land use component of the model to generate socioeconomic forecasts. Some key issues with respect to land use data that are important to consider as inputs to the model include an understanding of industry location choice decisions as a function of freight carrier transportation system performance, as well as a better representation of the inter-dependencies between land use and economic activity. An example is how increased economic activity in one industry sector may fuel the development of land uses associated with other industry sectors and vice versa. This tendency can be the location of automobile parts and accessory firms near automobile assembly plants and is often called clustering of industries.
Freight trucking companies models for use in transportation network information is a key input for economic activity models in order to assign the freight flows by mode to each modal transportation network. The network is represented in terms of links and nodes that provide connectivity between zones. Following are some key network attributes to consider in the model: capacity, size and weight regulations, hazardous material regulations, road closures and road speed limits.

The key feature of economic activity models is the integrated modeling of the dynamic interactions between economic activity, land use, and truck freight rates. The feedback of model results to the economic/land use model accounts for any changes in economic activity and/or land use that would result from future variations in transportation system performance. These changes in economic activity and land-use patterns in turn impact the magnitude and distribution of ltl truck freight flows on the transportation network and associated transportation system performance. Due to the considerations of these dynamic interdependencies between transportation system performance, economic activity, and land use, economic activity models also offer capabilities to accurately model induced freight demand impacts of new transportation or industrial investments.

The interrelationships between economic activity and land use are important to understand, particularly in developing freight shipping company forecasts, since land use defines the spatial distribution of economic activity, and economic activity has a significant impact on the location and types of land uses in a region. For example, increased port economic activity may impact the development of new warehousing/distribution center land use and their location patterns. In addition, new land uses and development also can help economic activity in a region, which underscores the importance of integrating land use forecasts with predicting economic activity and associated shipping freight demand. For example, the development of a new intermodal terminal in a region can instigate the development of logistics parks and warehouse/distribution centers, resulting in increased economic activity and associated demand for freight transportation.

For this model, the socioeconomic data available are stratified into the following 10 industry groups: 1) agriculture/farm/fishing, 2) mining, 3) construction, 4) manufacturing – products, 5) manufacturing – equipment, 6) transportation, 7) wholesale, 8) retail, 9) finance, and 10) education/government. The availability and use of multiple industry groups increases the accuracy for truck travel generation because each industry group can have a different freight shipping rate.

Trip distribution was performed using a standard gravity model. Model calibration was performed using a reasonableness check of the average truck trip lengths estimated by the model.

The truck model is designed to generate truck volumes based on average daily traffic. The truck model output reports truck volumes based on truck classes are medium-heavy duty and heavy-heavy duty for regulatory purposes.  A multi-class equilibrium assignment was performed and validated by comparing model truck volume outputs to observed truck counts collected.

Some of the issues in the San Joaquin Valley truck model that are being addressed in the ongoing model update include:

There were no calibration procedures adopted to validate the ITMS commodity flows to observed shipping companies truck counts.
Flows of nonmanufactured commodities (especially farm and mining products), flows between major city pairs (e.g., flows between the urbanized portions of Southern California and the San Francisco Bay Area), and flows disaggregated to the zip code level need more careful scrutiny and adjustment using a variety of other sources.

The secondary truck trip tables were developed using rates that were found to be too high and needed to be scaled back during calibration.

The discounted freight shipping rates for the internal truck model were developed from two primary sources of existing truck models. The data was selected because it provided freight truck discounts rates based on national averages. The Vancouver rates were selected to provide stratifications of rates for more employment categories. The data was used to derive rates for light trucks, while both the aforementioned sources provided rates for medium and heavy trucks, although the data defines these categories as six or more tire trucks and combination trucks, respectively. These rates were originally developed using the two primary sources of data, but were adjusted during model calibration.

Freight truck models develop highway freight truck rates by assigning an O?D table of freight truck flows to a highway network. The O?D truck table is produced by applying truck trip generation and distribution steps to existing and forecast employment and/or other variables of economic activity for analysis zones. This method involves estimating the O?D table directly using trip generation rates/equations and trip distribution models at the TAZ level. This is similar to the four-step passenger models. The mode choice step is eliminated since shipping freight truck trips are estimated directly without consideration of other possible modes for moving freight. The components required for this modeling technique include existing and forecast zonal employment data, methods to generate zonal freight productions and attractions by using freight truck trip generation rates, methods to generate truck O?D flows by applying trip distribution procedures to truck productions and attractions, and methods to assign the O?D freight truck flows to a highway network.

Freight truck models usually attempt to account for shipment of goods, including local delivery. Because these models are focused exclusively on the truck mode, they cannot analyze shifts between modes. Truck models are usually part of a comprehensive model that forecasts both passenger and freight movement and, consequently will often use a simultaneous assignment of truck trips with automobile trips.

The ability to assign the commodity vehicle tables to modal network will in large part depend on the quality of the modal networks and the ability to consider traffic by vehicles other than those shipping freight. The choice to use a commodity table in freight forecasting in lieu of trip generation and distribution typically is done because a more sophisticated model transportation model is not available. This quite often is accompanied by the lack of an auto highway model. Although a commodity table can be assigned directly to a highway network, but without the interaction of auto traffic, the response to congestion cannot be considered. For that reason, the use of commodity models is often accompanied by simple auto highway models. Auto trip tables were created through an Origin-Destination Matrix Estimation process using only observed traffic counts. Although this table does not allow the consideration of behavioral changes, its inclusion at least ensures that the combined impact of auto and truck congestion is considered. Georgia and Tennessee also approached the inclusion of nonfreight trucks in the freight forecasting process differently. Tennessee made the assumption that commodity trucks can be considered the same as large combination tractor trailers and assumes that observed single unit trucks could be considered to be the same as nonfreight trucks.

Shipping freight quote covered in Section 25

Methods to forecast freight demand involve the creation of flows of freight between zones, and trip tables, using trip generation and distribution steps. For urban models, trip tables (generally just for shipping companies trucks) are created by trip generation and distribution equations that are created from trip diaries or surveys of commercial vehicles or using the coefficients of others that have been developed from such surveys. Those statewide models that deal with commodity freight develop trip generation and distribution equations from surveys of commodity flows, such as the Commodity Flow Survey, the Freight Analysis Framework. Urban commercial vehicle surveys will always only be a statistical sample of all truck trips. However, even if commodity flow surveys are developed from statistical samples, they are generally expanded into complete flow tables, typically for an entire year. Since these commodity flow tables are themselves trip tables, if freight flow patterns are expected to be fairly stable, instead of using the commodity flows surveys as a means of developing trip generation and distribution equations, these commodity flow surveys themselves can be used as trip tables. This section discusses how commodity flow surveys can be used directly as trip tables in freight forecasting.

Although the organization of a commodity flow database might not look like a trip table to those who are familiar with travel demand models, its data fields easily can be reorganized into a trip table of freight flows. It contains as attributes origins and destinations, commodity type (purpose), and units of flow by mode. A sample frame of the database as used in the Tennessee Freight Model, where the records are identified by the origin, the destination, and the commodity (purpose). The flow for each of these records by mode is specified in annual tons.

The use of a commodity table in place of one developed through a trip-generation and trip-distribution process does have limitations. These forecasts are not easily modified in response to changes in employment forecast by industry or by specific units of geography. The freight flows will not change in response to changes in the transportation system that might result in new distribution patterns. The use of a fixed table for freight may represent a different paradigm than that used for passenger travel. The use of commodity tables directly for freight flows is often part of a less sophisticated model, where simplifications were for the passenger trip table. The direct use of commodity flow tables in transportation forecasts is typically done in state forecasting, since the internal truck movements that are of interest in urban travel forecasting are not represented in most commodity databases. The direct use of a commodity trip table may be considered for the external portion of the forecasting.

Discount freight is covered in section 15.

Site or facility planning is an essential component of a comprehensive freight shipping planning process. A large fraction of freight traffic flows in a region move to and from freight facilities like manufacturing plants, warehouse/distribution centers, or inter-modal transfer facilities. Consequently, development of a new facility or expansion of an existing facility can have a significant impact on the magnitude and spatial distribution of freight flows in a region.

Multi-modal access route planning is one of the most important elements in a site or facility planning process. This involves predicting mode-specific freight traffic demand generated by the facility and using these predictions for planning the development of multi-modal freight access routes to ensure efficient handling of demand by each access mode. Multi-modal access routes provide the critical link between the facility and the larger transportation network, and consequently, any bottlenecks on access routes can have significant impacts on facility operations for example, economic impacts associated with freight transportation delays.

The site or facility planning process can be subdivided into two broad steps thatinclude freight modeling and the planning applications step.  These steps are discussed in greater detail in the following sections, focusing specifically on planned sites or facilities. The planning approach for existing sites or facilities is relatively straightforward and involves collecting simple traffic counts and observing where and when these counts are taken, and using simple trend analysis or trip generation rates using existing counts to forecast freight flows on the network.