Trucking Freight

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Freight models in states that are geographically small and densely populated with adjoining urban areas, such as Connecticut and New Jersey, tend to take the form of urban truck models. Freight models in larger states, particularly those with larger rural areas and/or large percentages of pass-through traffic, such as Indiana, Florida, and Wisconsin, forecast freight in “four-step” commodity models, are a principal focus of this section. Still other states, such as Virginia, Tennessee, and Georgia, follow the general form of commodity model, but use acquired commodity freight tables in lieu of forecasting those tables in the trip generation and trip distribution.

State “four-step” commodity models are truly multimodal. The modes considered in these models typically include truck, rail, water, and air, even though the assignment step may only address trucks, and sometimes rail. As multimodal commodity models, the flow unit is common to all modes, and is typically tons.  These models tend to be calibrated from annual commodity flow tables and the forecasts in the first forecasting steps will be annual tons.

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Freight forecasting models, as all models, should have boundaries such that they internalize most of the trips that will be subject to forecasting. In the case of passenger modeling, these boundaries can be set at the jurisdictional boundaries of the state. Internal freight traffic within a state is typically no more than 25 percent of the flow total, and the flow to, from, and through the state due to national traffic comprise the majority of the freight flows. In order to properly forecast this traffic, the geographical area covered by state freight models typically is most of the continental United States, if not all of North America. The inclusion of modes that primarily travel distances of over 500 miles, such as rail, water, and air also suggests that the freight modal boundary should be much greater than just the state boundary. States that have developed “four-step” commodity freight models typically already have developed detailed travel-demand model zones and networks within the state boundary. These models and zone systems have been extended by inclusion of national highway and rail networks.

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Through proper management of your inbound freight, you can reduce your total purchase cost by 5% or more.  Integrating functions between the purchasing and transportation,  is essential to exploit the full savings in the  transportation environment.

As a general rule, the greater the volume purchased, the lower the cost per unit. The same holds true in the purchase of transportation services. The practice of consolidation your freight with just one or two freight carriers can give you higher freight volumes per carrier and therefore allowing you to receive a better discount rate. Developing a better working relationship with your carriers can help identify opportunities which are beneficial to both parties.

Outside of his professional duties at Reimer Express, which is a division of YRC, Gording has also served in director and executive roles, including president from 2006-08 for the Manitoba Trucking Association (MTA). He currently serves as past president. …

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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 carrier 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 DOTs, MPOs, 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 intermodal 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.

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 DOTs, MPOs, 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 intermodal 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 freight. It is most suitable for analyzing incremental changes in freight activity.

Payloads or truck loads are limited by weight and volume considerations. The commodities carried by freight carriers have different densities and therefore, different payloads for the same volume. Because of handling and packaging needs, payloads also may differ by commodity. For example, large size trucks carry heavier loads even for the same commodity. If payloads are calculated for different freight truck classes, the commodity tonnage needs to be allocated to the different truck classes. Smaller trucks tend to be used more in shorter-haul service. To the extent that length of haul and truck size are correlated, length of haul (directly available from commodity flow data) can be used in calculating payload factors. Payload factors can be calculated for loaded trucks only (estimated truck volumes must then be adjusted to account for percent of empties) or they can average empty and loaded weights.

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.

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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 obtained for both the dependent and independent variables over the course of several time periods like 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.

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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 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 (that may be affected by increased imports or exports or by changes in production processes);
• Modal choice (which may be affected by changing transport requirements or changing cost and service characteristics of competing modes);
• Facility usage per unit of freight volume (that may be affected by changes in shipment size or container size);
• 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.

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