Quick Response Freight Manual II
3.0 Simple Growth Factor Methods
3.1 Introduction
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 or receiving freight. It is most suitable for analyzing incremental changes in freight activity.
3.2 Growth Factors Based on Historical Freight Trends
Fitting historical data to a curve that can be used in forecasting is a topic that mathematicians would call linear or nonlinear regression, depending on the type of curve that is desired. This section presents simple procedures for using historical data for projecting future freight demand. The technique first describes a simple method using only two observations at different points in time, and then describes a method where the data available will be for many time periods. A regression of the line or curve can be found using statistical calculators, spreadsheet functions or, if available, statistical software packages.
3.2.1 Linear Growth
When assuming that freight flow grows in a linear fashion, also sometimes called proportional growth, the annual growth factor (AGF) rate will be the difference between the flow in the first observation and the flow in the second observation divided by the number of years between those observations:
AGF = (F_{2}-F_{1})/(Y_{2}-Y_{1})
where F_{1} is freight flow in year Y_{1} and F_{2} is freight demand in year Y_{2}.
The linear annual growth factor can then be applied to predict future demand (F_{3}) for some future year (Y_{3}) as follows:
F_{3} = F_{2}+AGF*(Y_{3}-Y_{2})
For example, assume that the number of truck trips at a given location on an average weekday was 8,000 in 2000 and 10,000 in 2005. Using this simple procedure, the forecast number of truck trips for the year 2010 is 12,000; i.e.,
AGF = (10,000-8,000)/(2005-2000) = 400
12,000 = (10,000)+(400)*(2010-2005)
If more than two years of historical data are available for the variable to be forecast, this data can be used to solve a linear regression according to the formula:
F(n) = Constant+AGF*(n)
where n is the number of years from the first observation and Constant and AGF are found from the linear regression. Table 3.1 shows an example using a the regression package in Excel (first turning on the Tools/Add-ins/Analysis Tool Pak, and then selecting the Tools/DataAnalysis/Regression) and the data organized in a column, where the x‑variable (independent variable) is the Years from 1993 and the y‑variable (dependent variable) is the Tons. In this application, the linear regression solutions of both the intercept and the x‑variable_{1} coefficients can be taken to be the Constant and the AGF, respectively.
In this case, with an R‑Square [R2 is a statistic that provides information about the goodness of fit of a model] of 0.812, the forecasting formula is:
F(n) = 104,739+1,357*(n)
and the results are shown in the last column of Table 3.1.
Year |
Tons |
Years from 1993 |
Linear Regression |
---|---|---|---|
1993 |
104,432 |
0 |
104,739 |
1994 |
111,955 |
1 |
106,096 |
1995 |
101,807 |
2 |
107,453 |
1997 |
109,659 |
4 |
110,168 |
2003 |
117,896 |
10 |
118,311 |
2004 |
120,266 |
11 |
119,668 |
2005 |
121,445 |
12 |
121,025 |
2010 |
[no data] |
17 |
127,811 |
2015 |
[no data] |
22 |
134,597 |
2020 |
[no data] |
27 |
141,382 |
3.2.2 Compound Growth
By assuming that freight flow grows in a compound fashion, such as a manner similar to compound financial growth, the annual growth factor will be the ratio of the flow in the second and first raised to a power which is the inverse of the number of years between the first and second observations:
AGF = (F_{2}/F_{1})^{1/(Y2-Y1)}
where F_{1} is freight flow in year Y_{1} and F_{2} is freight demand in year Y_{2}. This also can be expressed as a compound annual growth rate by subtracting 100 percent from the AGF.
The compound growth factor can then be applied to predict future demand (F_{3}) for some future year (Y_{3}) as follows:
F_{3} = F_{2}*AGF^{(Y3-Y2)}
For example, assume that the number of truck trips at a given location on an average weekday was 8,000 in 2000 and 10,000 in 2005. Using this simple procedure, the forecast number of truck trips for the year 2015 is 15,625; i.e.,
AGF = (10,000/8,000)^{1/5} = 1.04564
15,625 = (10,000) (1.04564)^{10}
and the compound annual growth rate can be interpreted as 4.6 percent (104.564 percent minus 100 percent).
If more than two years of historical data are available for the variable to be forecast, this data can be used to solve a power regression according to the formula:
F(n) = Constant*AGF^{(n)}
where n is the number of years from the first observation and Constant and AGF are found from the linear regression. Table 3.2 shows an example using a the regression package in Excel (first turning on the Tools/Add-ins/Analysis Tool Pak and then selecting the Tools/DataAnalysis/Regression). The data are organized in a column, where the x‑variable (independent variable) is the Years from 1993 and the y‑variable (dependent variable) is the Tons expressed as a natural logarithm, Ln(tons). In this application, the linear regression solutions of the both the intercept and the x‑variable_{1} coefficients have to be converted from natural logs to whole numbers by taking the exponential of those terms, e.g., Constant = Exp (intercept) and AGF = EXP (x‑variable coefficient).
In this case, with an R‑Square of 0.798 the coefficients are:
F(Y) = 104,794*(1.012)^{(n)}
and the results are shown in the last column of Table 3.2. This regression also can be interpreted as a compound growth rate of 1.2 percent (101.2 percent minus 100 percent) per year.
Year |
Tons |
Ln(Tons) |
Years from 1993 |
Compound Regression |
---|---|---|---|---|
1993 |
104,432 |
11.556 |
0 |
104,794 |
1994 |
111,955 |
11.626 |
1 |
106,064 |
1995 |
101,807 |
11.531 |
2 |
107,350 |
1997 |
109,659 |
11.605 |
4 |
109,970 |
2003 |
117,896 |
11.678 |
10 |
118,217 |
2004 |
120,266 |
11.697 |
11 |
119,650 |
2005 |
121,445 |
11.707 |
12 |
121,101 |
2010 |
[no data] |
[no data] |
17 |
128,623 |
2015 |
[no data] |
[no data] |
22 |
136,613 |
2020 |
[no data] |
[no data] |
27 |
145,099 |
3.2.3 Results
The historical regression can be done using either a linear growth or a nonlinear regression technique. The compound growth regression is only one of many nonlinear regressions can be done using any number of curve fitting techniques. Those interested in alternative techniques should pursue those elsewhere.
Linear growth will always be less than compound growth, as simple interest calculations in finance are always less than compound interest. The methods chosen should be consistent with the pattern of the observed data and for the intended purpose and should recognize the uncertainly of the forecast and the risk of the forecast being either too low or too high for the intended use. For example, a forecast of truck volumes to support pavement designs should be on the high side and compound growth may be preferred, while financial analysis such as tolling should be conservative on the low side and might be better suited for a linear regression.
The regressions can be successfully calculated even if observations are not available for all years, as shown above. Also, the regressions should only be used for a period consistent with the observations. By using these simple techniques to forecast growth for a period much longer than the observation, the assumption being made is that the underlying pattern will not change during the entire period, which may not be appropriate. In Tables 3.1 and 3.2, the forecast for 2020 is consistent with the period of observation but the forecast for 2025 is for a period longer than the observations and should be used with caution.
3.3 Growth Factors Based on Direct Economic Projections
This section presents a simple procedure for forecasting freight using projections of future demand or output for the goods being transported. It also describes various sources of economic forecasts that a freight analyst can use in applying this procedure as well as ways to improve its accuracy. A brief discussion of sensitivity analysis and alternative futures also is included.
3.3.1 Analysis Steps Explained
To simplify the approach for deriving forecasts of future freight traffic from economic forecasts, it can be assumed that the demand for transport of a specific category of freight, for example a commodity, is directly proportional to an economic indicator variable that measures output or demand for that category. With this assumption, growth factors for economic indicator variables, which represent the ratios of their forecast year values to base year values, can then be used as the growth factors for freight traffic.
This procedure requires data or estimates of freight traffic by category/commodity type for a reasonably “normal” base year, as well as base and forecast year values for the corresponding economic indicator variables. The basic steps involved in the process are as follows:
- Select the commodity or industry groups that will be used in the analysis. This choice is usually dictated by the availability of forecasts of economic indicator variables. These forecasts may be of economic activity, for example Gross State Product (GSP), or of employment of the industry groups associated with each category/commodity.
- Obtain or estimate the distribution of base year freight traffic by category/commodity and its associated industry group. This data might be available from an intercept survey of vehicles traveling on the facility for which forecast are being prepared. If actual data on the distribution are not available, state or national sources may be used to estimate this distribution. For example, the Census Bureau’s VIUS provides information on the distribution of truck VMT by commodity carried and industry group. [The Vehicle Inventory and Use Survey (VIUS) is a periodic survey of private and commercial trucks registered (or licensed) in the United States. It is a sample survey taken every five years as part of the Economic Census. The funding for the 2007 VIUS has not been budgeted. The 2002 VIUS may be the last survey available.] Determine the annual growth factor (AGF) for each commodity or industry group as follows:
AGF = (I_{2}/I_{1})^{1/(Y2-Y1)}
where I_{1} is the value of the economic indicator in year Y_{1} and I_{2} is the value of the economic indicator in year Y_{2}.
- Using the AGF and base year traffic, calculate forecast year traffic for each commodity or industry groups as follows:
T_{f} = T_{b}AGF^{n}
where n is the number of years in the forecast period.
- Aggregate the forecasts across commodity or industry groups to produce the forecast of total freight demand.
Alternatively, if the mix of traffic by industrial sector/commodity is not available and the national sources are not considered useful, the forecasts of employment may be converted to truck trips using available truck or vehicle trip rates for the economic indicator variable. In this case the method is as follows:
- Select the commodity or industry groups that will be used in the analysis. This choice is usually dictated by the availability of forecasts of economic indicator variables. These forecasts may be of economic activity; for example, GSP, or of employment of the industry groups associated with each category/commodity.
- Calculate the base year number of freight units, e.g., truck trips, for each sector based on the economic indicator variable and the freight units, e.g., truck trip rates for that sector. Calculate the forecast year number of truck for each sector based on the economic indicator variable and the truck trip rates for that sector.
- Sum all of the truck trips for the base year and for the forecast year. Determine the total AGF as follows:
AGF = ((ΣI_{2}*FR)/(ΣI_{1}*FR))^{1/(Y2-Y1)}
where I_{1}*FR is the value of the economic indicator times the flow rate (e.g., truck trip rate) for that economic indicator in year Y_{1} and
I_{2}*FR is the value of the economic indicator times the flow rate (e.g., truck trip rate) for that economic indicator in year Y_{2}.
- Apply the total growth rate to the base freight flow to determine the future freight demand.
The most desirable indicator variables are those that measure goods output or demand in physical units (tons, cubic feet, etc.). However, forecasts of such variables frequently are not available. More commonly available indicator variables are constant-dollar measures of output or demand, employment, or, for certain commodity groups, population or real personal income. The following subsection describes the data sources for forecasts of some of these economic indicator variables.
3.3.2 Sources of Economic Forecasts
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 substate 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). [The most recent BLS forecasts are contained in U.S. Department of Labor, Bureau of Labor Statistics, Employment and Output by Industry, 1994, 2004, and Projected 2014, http://www.bls.gov/emp/empinddetail.htm.]
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 analyses. 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 (MSA), 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.
3.3.3 Improving the Demand Forecasts
The basic procedure presented above makes the simplifying assumption that, for any transport facility, the percentage change in demand for transport (i.e., freight traffic) of each commodity group will be identical to the percentage change in the corresponding indicator variable. However, for various reasons, the two percentage changes are likely to be somewhat different from each other. These reasons include changes over time in:
- Real value of output per ton, adjusted for inflation;
- Output per employee; also known as labor productivity;
- Transportation requirements per ton; and
- Competition from other facilities and modes.
To the extent that the likely effects of these changes are understood and can be estimated at reasonable cost, the basic procedure should be modified to reflect these effects. These effects are discussed below.
For most commodity groups, the relationship between value of output (measured in constant dollars) and volume shipped (measured in pounds, tons, cubic feet, etc.) may change over time. These changes may be due to a change in the mix of commodities being produced within a given commodity group (e.g., more aluminum and less steel) or a change in the average real value per ton of major products within the group. These changes may result in changing value per ton in either direction. For example, the shift to flat screen panel televisions from cathode ray tube televisions provides an important example of a product category. Computers, in which the value per ton, or per pound, has decreased appreciably. When transport demand is being forecast for several different commodity groups, adjustments for expected changes in value per ton for all commodity groups will be relatively expensive to make and may not have a very significant effect on the overall forecast of transport demand. However, when there are one or two commodity groups that are of particular interest, some consideration should be given, at least in an informal way, to determine how real value per ton for these groups has been changing and how it is likely to change over the forecast period.
Employment is related to transport demand less closely than is real output. Hence, employment is a less desirable indicator variable. However, because long-term forecasts of employment are more available than forecasts of output, employment forecasts must be used for some purposes. As a result of improvements in labor productivity, real dollar-valued output per employee increases over time, and physical output (in tons or cubic feet) tends to increase as well. Forecasts of the overall increase in real dollar-valued output per employee for goods-producing industries (agriculture, mining, construction, and manufacturing) can be obtained from the public and private sources listed above, but should consider the cyclical nature of commodity prices. In order to avoid a downward bias in the forecasts of transport demand, forecasts of percentage change in employment should be converted to forecasts of percentage change in (real dollar-valued) output by multiplying by estimated compound growth in labor productivity over the forecast period. Additionally, changes in production methods that result in a reduction in domestic employment, such as a shift to off-shore manufacturing, may change the origin and distribution and freight, but not the overall shipments.
Decreases in the real cost of transportation that have occurred over time have resulted in a general tendency for industry to increase its consumption of transport services in order to economize on other factors of production. This tendency has resulted in trends toward decreased shipment sizes and increases in both lengths of haul and standards of service, with the last effect resulting both in a demand for premium quality services (e.g., just-in-time delivery,) provided by traditional modes and in the diversion to more expensive modes that offer faster, more reliable service. In recent years, these decreases have been offset by increases in certain components of costs, particularly fuel costs. Recognizing that shippers will use transportation services that are the most cost-effective, any changes in transportation and inventory costs may result in changes in the distribution pattern or mode that is used by those shippers.
Finally, whenever relevant, forecasts of demand for a facility or mode should be adjusted to reflect expected changes in degree of competition from other facilities or modes. These changes may result from:
- Expected changes in relative costs;
- The elimination of base year supply constraints at the facility in question or at competing facilities;
- The development of future supply constraints at the facility in question or at competing facilities; or
- The development of new competing facilities.
The forecasting problems posed by base year supply constraints frequently can be avoided by choosing a base year when no significant supply constraints existed. When this is not practical, a combination of historic data and judgment may be used to adjust the estimates of base year facility usage to eliminate the effects of the supply constraints, thus producing estimates of base year demand in the absence of supply constraints; annual growth rates or growth factors can then be applied to these estimates of base year demand to produce the forecast demand.
3.3.4 Sensitivity Analysis
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 (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.
3.3.5 Alternative Forecasting Methods
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.
3.3.6 Illustrative Example
The State of Minnesota used an economic factoring method to forecast truck flows on its Truck Highway (TH) system. For the TH 10 segment through Sherburne, Anoka, and Ramsey counties [IRC TH 10 Corridor Management Plan: TH 24 in Clear Lake to I‑35W, prepared by Howard R. Green Company for the Minnesota Department of Transportation – Metro Division and Minnesota Department of Transportation – District 3, May 2002], the State followed the following steps:
- Determined the base year truck volumes on individual sections of the corridor between major intersections interchanges from historical traffic counts;
- Obtained existing industrial employment by sector by county from the Minnesota Department of Employment Security (DEED);
- Obtained regional industrial employment projections for central Minnesota and the Twin Cities metro areas from the Minnesota Department of Employment Security, now the Minnesota Department of Employment and Economic Development (DEED);
- Developed county employment by industry for the base and forecast year by assuming that county employment is proportional to regional employment by sector;
- Converted the county employment forecast to truck trips based on rates shown in Table 3.3;
- Calculated the growth in trucks trips between the base year of 1999 and 2020 for each county by applying these rates to the existing and forecast county employment; and
- Applied those growth rates to the base year truck volumes on TH 10 depending on the county in which it is located.
SIC |
Description |
Trips/Employee |
---|---|---|
1-9 |
Agriculture, Forestry, and Fishing |
0.500 |
10-14 |
Mining |
0.500 |
15-19 |
Construction |
0.500 |
20-39 |
Manufacturing, Total |
0.322 |
40-49 |
Transportation, Communication, and Public Utilities |
0.322 |
42 |
Trucking and Warehousing |
0.700 |
50-51 |
Wholesale Trade |
0.170 |
52-59 |
Retail Trade |
0.087 |
60-67 |
Finance, Insurance, and Real Estate, Total |
0.027 |
70-89 |
Services |
0.027 |
80 |
Health Services (Including State and Local Government, Hospitals) |
0.030 |
Not Applicable |
Government |
0.027 |
Source: Minnesota DOT.
The results of these calculations are shown in Table 3.4. The employment forecast was converted to trucks trips by industrial sector prior to calculating growth factors, in lieu of calculating VMT by commodity. The assumption was made that the growth in truck traffic for the segment of TH 10 in each county could be forecast completely by the growth in employment in that county, converted to truck trips. No consideration was given to trucks that might only be passing through these counties.
Location: |
Location: |
Location: |
Growth: |
Growth: |
2020 Projections |
2020 Projections |
---|---|---|---|---|---|---|
MN 25 |
MN 24 (Becker) |
Sherburne |
39% |
30% |
866 |
1,165 |
MN 25 (Becker) |
MN 25 (Big Lake) |
Sherburne |
39% |
30% |
862 |
1,350 |
MN 25 (Big Lake) |
CR 14/15 |
Sherburne |
39% |
30% |
902 |
1,462 |
CR 14/15 |
TH 169 |
Sherburne |
39% |
30% |
1,022 |
1,940 |
TH 169 |
MN 47 |
Sherburne |
39% |
30% |
1,560 |
1,726 |
TH 169 |
MN 47 |
Anoka |
18% |
8% |
1,560 |
1,726 |
MN 47 |
TH 610 |
Anoka |
18% |
8% |
3,019 |
2,763 |
TH 610 |
MN 65 |
Anoka |
18% |
8% |
[no data] |
2,409 |
MN 65 |
I‑35 |
Ramsey |
8% |
8% |
[no data] |
1,979 |
I‑35 |
I‑694 |
Ramsey |
8% |
8% |
[no data] |
1,610 |