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27 CHAPTER 6 Forecasting Models This Toolkit focuses on the five model classes for account for the diversion of flow from that facility to other statewide freight forecasting listed in Section 4.0: the flow routes or modes. factoring method, the O-D factoring method, the truck The flow factoring method relies on regression equations, model, the four-step commodity model, and the economic which may be based on two methods: time series analysis and activity model. These model classes share many of the same economic analysis. Time series analysis involves an examina- components, differing from each other primarily in their tion of the historic flows on a transportation facility, with organization and use of these components. The key differ- only time as an indicator variable. Economic analysis uses ences between the five classes also are described in this economic variables as indicator variables to explain the his- section. torical facility flows. Both methods are described below. Time Series Analysis 6.1 The Direct Facility Flow Factoring Method Time series analysis is a means of understanding data vari- ability over time. Because a time series model exclusively Description represents past events and relationships, it can be used to As shown in Figure 6.1, the direct facility flow factoring forecast the future as long as the future is expected to behave method provides freight volumes on transportation system like the past. Time series analysis is particularly appropriate links such as roads, railroad tracks, and ports. The method re- when the forecast is short term and insufficient time and quires information about the facility itself and some forecasts resources exist to build and calibrate a behavioral model. of the factors affecting the facility. Time series models can be used for modal, policy, and data Although flow factoring is often used in individual project considerations. planning, it neither provides overall system forecasts nor con- A simple time series analysis fits a straight line to a series of siders many factors important in freight forecasting. How- annual observations of freight flows, such as annual tons ever, the method may be appropriate for developing forecasts shipped through a port. Many statistical software packages, for special generators, such as ports, within a more complex such as SAS or SPSS, or even spreadsheet programs such as model. Microsoft Excel have regression features to develop equations The facility flow factoring method is used to rapidly apply that can be used to forecast future freight flows based on the existing data to determine one or several forecast volumes. observed data. Usually, the method is intended for short-term forecasts; many assumptions are needed to make it work effectively and Economic Analysis its range of applicability is limited. Flow factoring is relatively simple, however, and commonly used by state departments Economic analysis can be used to forecast changes in of transportation across the United States. The method can freight demand due to changes in the level of economic be divided into two general classes: one that produces future activity or related factors. Forecasting based on growth in estimates of flow on a facility based on applying growth fac- economic factors is useful because it recognizes the fact that tors to the flow on that facility, and one that produces esti- demand for freight transportation is derived from underlying mates of flow on a facility based on applying factors that economic activities. The economic analysis method relies on
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28 where I1 is the value of the economic indicator in year Y1 and I2 is the value of the economic indicator in year Y2. 4. Using the annual growth factor and base year traffic, cal- culate forecast year traffic for each commodity or indus- try groups as follows: Tf = Tb AGFn where n is the number of years in the forecast period. 5. Aggregate the forecasts across commodity or industry groups to produce the forecast of total freight demand. The most desirable indicator variables are those that meas- ure goods output or demand in physical units (tons, cubic feet, etc.). However, forecasts of such variables frequently are not available. More commonly available are constant-dollar Figure 6.1. The flow measures of output or demand, employment, or, for certain factoring method. commodity groups, population or real personal income. The following subsection describes the data sources for forecasts of some of these economic indicator variables. forecasts of changes in economic variables to estimate the corresponding changes in freight traffic. To simplify the approach for deriving forecasts of future Data Sources of Economic Forecasts freight traffic from economic forecasts, the demand for trans- Analysts at state departments of transportation, MPOs, port of a specific commodity is assumed to be directly pro- and other planning agencies may use several sources to obtain portional to an economic indicator variable that measures estimates of growth in economic activity, by geographic area output or demand for the commodity. Consequently, growth and industry or commodity type. factors for economic indicator variables, which represent the Many states fund research groups that monitor the state's ratios of their forecast year values to base year values, can be economy and forecast changes. For example, the Center for used as the growth factors for freight traffic. the Continuing Study of the California Economy develops 20-year forecasts of the value of California products by two- Economic Analysis Process digit SIC code. The Texas Comptroller of Public Accounts develops 20-year forecasts of population for 10 substate Economic analysis requires data or estimates of freight regions and 20-year forecasts of output and employment by traffic by commodity type for a reasonably normal base year, one-digit SIC code and substate region. A private firm pro- as well as base year and forecast year values for the corre- duces 20-year forecasts of output and employment in Texas sponding economic indicator variables. The basic steps by three-digit SIC code. involved in the process are as follows: Long-term economic forecasts also are available from two Federal agencies. At two-and-one-half-year intervals, the 1. Select the commodity or industry groups that will be used Bureau of Labor Statistics (BLS) publishes low, medium, and in the analysis. This choice is usually dictated by the avail- high 12- to 15-year forecasts of several economic variables ability of forecasts of economic indicator variables. Much including real domestic output, real exports and imports, and of the available forecasts are by SIC code. employment for each of 226 sectors generally correspon- 2. Obtain or estimate the distribution of base year freight ding to groups of three-digit SIC industries. Also, at five-year traffic by commodity or industry group. If actual data on intervals, the Bureau of Economic Analysis (BEA) develops the distribution are not available, state or national sources 50-year regional projections of population and personal may be used to estimate this distribution. For example, the income as well as employment and earnings by industry sec- U.S. Census Bureau's VIUS provides information on the tor. The BEA forecasts are published by state for 57 industries, distribution of truck vehicle-miles traveled by commodity and by metropolitan statistical area and BEA economic area carried and industry group. for 14 industry groups. 3. Determine the annual growth factor (AGF) for each com- Short- and long-term economic forecasts are available modity or industry group as follows: from several private sources as well. The private firms use AGF = (I2/I1)1/(Y2-Y1) government and industry data to develop their own models