An Econometric Approach to Forecasting Vehicle Miles Traveled in Wisconsin

Transcription

1 0 0 0 An Econometric Approach to Forecasting Vehicle Miles Traveled in Wisconsin Mike Sillence Traffic Forecasting Section Wisconsin Department of Transportation 0 Sheboygan Avenue, Room 0 Madison, WI 0- Phone: (0) - mike.sillence@dot.wi.gov Submitted July st, 0 Word Count:, Figures and 0 Tables, Total Words

2 Mike Sillence ABSTRACT Just as estimating vehicle miles traveled (VMT) is important in understanding past and current highway system performance, forecasting VMT is critical in predicting future system performance. In Wisconsin, both travel demand models and regressions are used to forecast traffic at individual locations. The imminent completion of a renovated statewide travel demand model will enable forecasting VMT at the statewide level, and it was determined that a complementary method, similar to how the location-specific regressions complement the metropolitan area travel demand models, would be beneficial. While the location-specific regression forecasts could be aggregated, they are inherently static and increasingly unreliable due to data constraints. A multiple linear regression model based on time series data from 0 through 0 for three explanatory variables was created to forecast annual percent change in statewide VMT in Wisconsin. The explanatory variables consist of annual percent change of each of Wisconsin employment, Wisconsin population, and regular U.S. gasoline prices. The model is conditional; the results depend on the forecasted values for the explanatory variables. These variables created the model with the best fit, and allow for scenario testing for budget, planning, and air quality purposes. The results of model indicate that VMT in Wisconsin will continue increasing at a decaying rate through the forecast year of 00. Various sensitivity tests were completed to determine the likely range of potential outcomes. This model will complement other methods for forecasting and estimating VMT in Wisconsin.

3 Mike Sillence INTRODUCTION Future VMT is measured the same as past and current VMT: it is the summed product of segment traffic volume (in annual average daily traffic or AADT) multiplied by roadway segment length for each segment within a specified area. In Wisconsin, VMT is estimated and forecasted statewide, which includes the aggregate of all roads, regardless of jurisdiction. In urban areas, travel demand models are used to forecast VMT as well as link volumes; a statewide travel demand model, currently under renovation, will make it possible to forecast VMT in detail throughout the state. When forecasting link volumes, location-specific regression of count data is used in conjunction with the demand models to produce a forecast. Using multiple tools in this way increases confidence in the results and allows for easier identification of potential problems. The goal in creating an econometric VMT forecasting model is to apply a similar method to VMT at the statewide level to validate and compare results. In addition to validating a statewide travel demand model, the results from the VMT forecast model could be used in budget forecasting, gas price scenario testing, and validating the Wisconsin Department of Transportation (WisDOT) annual VMT estimate. Traffic Forecasting and VMT estimation procedures are WisDOT Planning Manual (). METHODOLOGY AND DATA WisDOT uses a different method in estimating past VMT than in forecasting future VMT (). While WisDOT VMT estimates for past years use weighted roadway counts and fuel consumption and efficiency figures, the WisDOT VMT forecast is created using a static multiple regression time series model (referred to through the remainder of this paper as the WisDOT model, to distinguish it from WisDOT s travel demand models and other VMT models) that produces a conditional forecast, i.e. the forecasted VMT is conditionally based on the forecasted values for each independent variable. Both the historical VMT estimate and the VMT forecast are measured in yearly percent change in VMT, not the level of VMT itself. The factors for both the historical estimate and the independent variables for the forecast are measured in annual percent change as well. The WisDOT model s independent variables include: Annual percent change of real regular gasoline price (in dollars) Annual percent change of Wisconsin population Annual percent change of Wisconsin employment (total number of employees) Each variable s source contains data from 0 through 0. Gas prices were taken from the U.S. Department of Energy s Energy Information Administration (EIA) Monthly Energy Review Table. Retail Motor Gasoline and On-Highway Diesel Fuel Prices, these are national figures, equivalent Wisconsin-specific data is not available (). Gas prices were converted to dollars using the U.S. Bureau of Labor Statistics U.S. Transportation Consumer Price Index for All Urban Consumers (). Wisconsin population figures were taken from the Wisconsin Department of Health Services (DHS) Office of Health Informatics and the U.S. Census Bureau ()(). Wisconsin employment figures were taken from the U.S. Department of Commerce s Bureau of Economic Analysis GDP & Personal Income Regional Dataset (). The source of the forecasted values of these variables may be different than the source of the historical values; sources for future year data are outlined in the Results section. The WisDOT model is intended to be updated approximately every five years, based on the availability of new input data. Updates will include new input data for years that have occurred since the last update, investigation of new data sources and independent variables, and investigation of changing the historical and horizon years included in the WisDOT model. The final WisDOT model takes the form of:

4 Mike Sillence Where n is the forecast year, Gas Price n is the percent change in price of regular gasoline in dollars in year n, Employment n+ is the percent change in number of people employed in Wisconsin in the year after n, Population n+ is the percent change in population of the state of Wisconsin in the year after n, and VMT n is the percent change in VMT in year n. It was discovered during testing that both percent change in population and percent change in employment are lagging indicators of percent change in VMT. Using n+ instead of n drastically increased the fit and predictive power of those variables. This was not the case for percent change in gas price. Figure below displays the results of the multiple regression equation: Regression Statistics Multiple R 0.0 R Square 0.0 Adjusted R Square 0. Standard Error 0.0 Observations ANOVA df SS MS F Significance F Regression E- Residual Total 0.00 Coefficients Standard Error t Stat P-value Lower % Upper % Intercept % Employment E % Population % Gas Prices FIGURE VMT Forecast Equation CREATING THE WisDOT MODEL The WisDOT model was estimated using statistical software, and the results were validated using a second package. The data was stored and manipulated in spreadsheets. Several drafts were tested before the current version was determined final. A first draft of the WisDOT model used levels of VMT and the independent variables; however this was changed to annual percent change of VMT (and the independent variables) to avoid autocorrelation issues. Annual percent change in per-capita VMT was also considered as the dependent variable, however it was determined that population has a relevant relationship with VMT and needed to be included as an independent variable. With annual percent change of VMT as the dependent variable, a linear functional form was determined most appropriate. None of the variables had to be transformed to fit a linear relationship. Several potential independent variables were identified and tested during model specification, and the three variables included in the final model were those with a statistically significant relationship with VMT. As mentioned above, both annual percent change in population and annual percent change in employment appeared to be lagging indicators of annual percent change in VMT. As such, the year n+

5 Mike Sillence was used in those terms. The resulting model (the WisDOT model) had the lowest standard error, highest adjusted R, and statistically significant independent variables whose relationships with VMT can be readily explained. The final model had the best (lowest) Akaike Information Criterion (AIC) value, a statistical method used to compare similar models based on fit and number of independent variables. Other variables tested include the annual percent change of: Real personal income Vehicle registrations State trunk network lane miles Number of households in Wisconsin Wisconsin retail sales The term was included, and a Durbin-Watson test was conducted, to test for first-order autocorrelation; both tests indicated that autocorrelation is not present and the term was subsequently removed after it was shown not to be statistically significant. The standard error of the regression is approximately 0.0. This indicates that the standard deviation of error, which is the difference between the modeled change in VMT and the actual change in VMT, is 0.0. The number of observations,, is the number of years for which the variables have data. While potential multicollinearity exists between population and employment, the standard errors of each coefficient are sufficiently small although the standard error of population, the most elastic variable, is approaching the high side and the WisDOT model is intended primarily to study the gas price variable in scenario testing. Because neither population nor employment have multicollinearity issues with real gasoline prices, it remains possible to test different gasoline prices independently with this equation. Each independent variable in the WisDOT model has a sufficiently low P-value to be considered statistically significant at the % significance level. Each independent variable is also practically significant, which is to say that changes in the estimates sufficiently changes the forecast to be considered relevant, and each has the intuitively correct sign. The P-value for the F-test is also sufficiently low to be considered statistically significant at the % significance level. Each independent variable is consistently forecasted by trusted sources, which allows the WisDOT model to be applied consistently from year to year. The sources for the independent variable forecasts are similar to the sources of the data used in Wisconsin s travel demand models, however they are used in a slightly different, more aggregate way. Finally, the independent variables are distinct from those in the VMT estimation procedure (which uses the annual change in vehicle counts in Wisconsin and the annual change in Wisconsin s fuel consumption and fuel efficiency), which allows the two methods to complement one another (). After the WisDOT model was estimated, future year data for the independent variables was collected. The data can be inserted into the WisDOT model for any year for which forecasts exist, however since the WisDOT model was specified for annual percent change, each year up to the final forecast year must have data. The annual percent change in the Motor Vehicle Fuels, Lubricants, and Fluids Index from the IHS U.S. Economic Outlook April 0 report was used as the future annual percent change in regular gas prices (). This index is also used by other models at WisDOT and as an index, does not need to be adjusted for inflation. Wisconsin population forecasts were collected from the Wisconsin Department of Administration (DOA) Demographic Services Center December 0 report Wisconsin s Future Population. Wisconsin employment forecasts were derived from Woods & Poole Economics 0 State Profile Wisconsin, which is also the source for Wisconsin s travel demand models future employment forecasts ()(). The forecasted employment rate was taken from Woods & Poole and applied to the Wisconsin DOA population forecasts to determine the future employment levels. RESULTS Applying the WisDOT model to consecutive years from 0 through 0 yields a 0 VMT of. billion, which is 0.% higher than the estimated VMT of.0 billion. Applying the WisDOT model to

6 Mike Sillence consecutive years from 0 through 00 yields a VMT that will be approximately.0 billion by the year 00. This is a % increase from the 0 level, with an average change of.% per year (or weighted average growth of.%). This is significantly less than the prior years, which saw nearly a % increase and an average change of.% per year (or weighted average growth of.%). The continued increase is caused by stabilized gas prices, an increasing population, and a slightly higher employment rate. The forecasted rate of growth slows during the forecast period, with the last five years experiencing only 0.% annual change. This is largely due to a forecasted stabilization of Wisconsin s population and employment rate. Figure displays the results of the WisDOT model, including estimated VMT from 0 through 0 and forecasted VMT from 0 through 00. In Figure, the WisDOT model results are indicated by the red line, while the annual VMT estimation procedure results that were used to estimate the WisDOT model are indicated by the blue line. The WisDOT model is specified to match the change (slope) from year to year, not the values (levels). The WisDOT model nicely explains both the dip in the late 0 s and early 0 s and the more recent plateau over the last decade. 0,000 0,000 0,000 0,000 0,000 0,000 0,000 VMT Forecast 0,000 0,000, FIGURE : VMT Forecast ANALYSIS According to the WisDOT model, the primary cause of the recent plateau in VMT is a combination of real gas prices that spiked in the mid 000s, increasing % from 00 to 00, and a stagnation of the employment rate from 00 through 0. Wisconsin s population also grew at the slowest rate in almost 0 years between 00 and 0. The same trend occurred in the late 0 s and early 0 s, although to a smaller degree. Gas prices have since stabilized, and are projected to remain at or below current prices until at least 00. Wisconsin s employment has been slower to recover, however it is expected that by 00, the employment rate will have recovered to levels from the early and mid 000 s. The price of gasoline and the employment rate are both economic indicators that tend to fluctuate cyclically, while Wisconsin s population has consistently trended upward (only saw a decrease in population in Wisconsin since 0). According to the EIA s Annual Energy Outlook 0 (), the market share of all-electric and alternative-fuel light-duty vehicles is expected to remain relatively low through 00, peaking at % of new vehicle sales, indicating that gasoline will remain the dominant fuel source. The report also states

7 Mike Sillence that heavy-duty vehicle VMT will increase more quickly than light-duty vehicle VMT. The WisDOT VMT estimation procedure uses diesel fuel consumption in comparison to gasoline consumption to distinguish between heavy and light-duty vehicles, however there is a small but growing market share of alternative-fuel heavy-duty vehicles that are not captured using this method. Further research into whether other fuel sources could be used in conjunction with diesel is necessary. The Wisconsin DOA Services Center population forecasts used in the WisDOT model are conservative in comparison to the Woods & Poole Wisconsin 00 population forecasts ()(). The DOA forecasts the population to be just under. million by 00, while Woods & Poole forecasts it to be nearly million. The IHS 0 gasoline price forecasts are higher than the Energy Information Administration forecasts, which top out at $.0 (in 0 dollars) in 00; IHS forecasts peak at around $ (in 0 dollars) in 00 (). Woods & Poole employment forecasts are possibly less conservative, given their population forecast, however the employment rate used to create the change in employment for the WisDOT model is relatively conservative, increasing from % in the mid 000s to % in 00 (). The employment rate grew over three times as fast from through 000. The sensitivity testing described below was completed in part to ease concerns regarding potential inconsistencies in data sources. The total VMT forecast for Wisconsin is slightly higher than the national forecasts produced by the EIA, which grows about.% annually through 00, and lower than the FHWA composite weighted annual average VMT growth of.% through 0 published in the 0 Conditions and Performance report ()(). The EIA forecasts are used for federal budgeting and planning, while the FHWA projections are simply a composite of forecasts submitted by each state. According to the EIA, much of the growth will be attributed to commercial and freight truck VMT, which grows at over twice the rate of light-duty vehicle VMT (). According to the WisDOT model results, per-capita VMT is projected to increase annually at an average of % per year, less than the.% rate of increase since the 0 s, peaking at just over,0 in 00. The rate of increase in per-capita VMT declines over the forecast period and, using polynomial extrapolation, plateaus in 0 at,0 miles driven per-capita before slowly declining again. Several states currently have VMT statistics comparable to those forecasted for Wisconsin in 00. According to the USDOT Research and Innovative Technology Administration s Bureau of Transportation Statistics, Alabama s VMT per-capita was, in 0 while North Dakota and Mississippi s VMT per-capita were, and,0, respectively (). Indiana had a 0 population of. million, the same as Wisconsin s forecasted 00 population, and its 0 VMT was. billion. In 00, the population of Virginia was slightly higher than Wisconsin s 00 forecast and its total VMT was just over billion. The expected increase in commercial VMT could make the per-capita VMT higher in many states, including Wisconsin. Some degree of uncertainty exists in all forecasting, but one of the major questions with this type of model is how likely is Wisconsin s 00 VMT to hit.0 billion? This is a difficult question to answer, the model as it is specified is meant to produce elasticities for each of the independent variables. The elasticities indicate the most probable aggregate reaction of VMT if one of the independent variables change, i.e. if gas prices go up X% then VMT will likely go down Y%. The final VMT forecast is dependent both on the elasticities being accurate and the independent variable forecasts being accurate. Both the elasticities and some scenario testing are described in the following section. SENSITIVITY TESTING Several tests were completed as part of the results analysis to measure how the WisDOT model results change under different conditions. The first set involved the population parameter. While the population forecast is considered conservative, the increase in VMT per-capita was interesting. A test was made to investigate the results should per-capita VMT arbitrarily remain steady at 0 levels (,00 VMT percapita). The results indicated that forecasted 00 VMT would decrease by % ( billion to billion, which is still % higher than 0 VMT) under these conditions. Forecasted 00 VMT would decrease by % ( billion to. billion) from the WisDOT model forecast if the population forecast reached

8 Mike Sillence the higher Woods & Poole number and per-capita VMT remained steady. If per-capita VMT increases at the rate projected by the WisDOT model and the population reaches the Woods & Poole number, the total VMT would increase by % ( billion to. billion) from the original forecast (). The second set of tests investigated the employment parameter. For the travel demand models maintained by WisDOT, total employment forecasts from Woods & Poole are used (). The WisDOT model uses the same data, however Woods & Poole also creates population forecasts, which are not used; Wisconsin DOA population forecasts are used instead. The WisDOT model was specified using the employment rate from the Woods & Poole data, applied to the population forecast from Wisconsin DOA. A test was conducted to measure the effect of using the total employment, rather than employment rate, from Woods & Poole. The results indicate that forecasted 00 VMT would increase by % to billion under these conditions. If population reaches the Woods & Poole 00 number as well, forecasted 00 VMT increases by % to. billion ()(). The final set of tests measure the effect of future gasoline prices. The more conservative IHS index was used instead of the EIA forecasts, and the WisDOT model shows a % increase in 00 VMT (to billion) if the lower EIA forecasts are used. The IHS index ultimately projects just under $. per gallon of regular gas in 00, while EIA projects about $.0 per gallon (both in 0 dollars) ()(). Arbitrarily doubling the IHS index decreases forecasted 00 VMT by about % to. billion. This relatively small effect speaks to the inelasticity of demand for gasoline and the effects of the Corporate Average Fuel Economy (CAFE) standards that should help to curb demand. The sensitivity tests, as well as the independent variable coefficients (which are themselves elasticities), show that population has the greatest effect on VMT of the independent variables. The effect of employment is somewhat lower and gas price is much lower. The elasticity of demand for gasoline determined by the WisDOT model, -0.0, is very similar to the -0.0 estimated by the mid variance static model (that also used EIA gas prices), produced by C.-Y. Cynthia Lin and Lea Price in Gasoline price volatility and the elasticity of demand for gasoline (). A Washington VMT forecast model created by the Washington State Department of Transportation (WSDOT) in 0 determined the elasticity of demand for gasoline price was -0.0, slightly less elastic than the WisDOT model s -0.0 (). According to the Victoria Transport Policy Institute, short-term price elasticity of demand for gasoline price is just about -0. (). The elasticity of employment was found to be 0.0 in the Washington model, very comparable to the 0. found in the WisDOT model. The Washington model used vehicle registrations in place of population so those results are not directly comparable (). A search of the literature for population elasticities yielded no results. Even under the combined effect of stagnant per-capita VMT, an employment rate that remains steady at the 0 level, and a doubling of fuel prices, the WisDOT model forecasts that total VMT will exceed its previous record high within the forecast period. Under the opposite conditions, including cheaper gasoline and a higher than expected population increase, VMT could reach a new record high as soon as 0. The Washington model forecasts.% average percent change through 0, almost identical to the results of the WisDOT model (). A quick review of other states found no other comparable VMT forecast methods, perhaps statewide travel demand model results could be collected as part of a future effort. As suggested in the EIA Annual Energy Outlook 0, much of this VMT growth is likely to occur in commercial and industrial vehicle use, rather than personal light-duty vehicle use (). Commercial VMT information for Wisconsin is not available before the 0 s, which makes specifying a commercial VMT-specific model problematic. Exploration of splitting the WisDOT model into personal and commercial components should be studied in the future. CONCLUSION While the future is ultimately unknown, the WisDOT model was created using the best available methods to serve WisDOT s needs. The results are best used with the knowledge of the many assumptions that accompany them. Any number of things could drastically change the paradigm of driving in Wisconsin, including the introduction of autonomous vehicles, a change in energy sources or policy, or a widespread

9 Mike Sillence shift in personal preferences that curbs demand (or supply) for more vehicle travel which, according to a group of professors at the University of Connecticut, many believe is currently happening (). Their 0 paper synthesizes dozens of sources regarding changes in VMT and driving patterns, and concludes that the reasons for recent changes in VMT are more complex than in the past and are still not well understood. A focus of the paper is on the decoupling of VMT on the national scale from the economy, in terms of gross domestic product (GDP), in recent years and they look to the literature for potential explanations (). The WisDOT model challenges the notion that VMT and the economy are in fact decoupling. The more recent changes in VMT are actually explained more accurately than many of the changes during the earlier, more steadily growing periods. Part of this probably lies in the definition of the economy, GDP alone has little inherent explanatory power over travel compared to employment and travel costs. There are many underlying factors regarding VMT, those deemed most significant in its recent changes according to the literature are shifting preferences in younger generations, the implications of increased urbanization, and saturation in driving and car ownership (). These kinds of factors are very difficult to measure and more time is necessary to understand their relationships with VMT. While the WisDOT model as it is currently specified is not relevant to other geographies, the procedures of implementation, resulting growth rates, and elasticities should be (should the same independent variables be included). Along with breaking out commercial and personal VMT and investigating other fuel sources as mentioned in earlier sections, potential improvements to the WisDOT model should be pursued. For example, the Washington model exchanges population for vehicle registrations, which at present does not provide additional explanatory power in Wisconsin, but may in other areas (). An additional substitute for population could be age cohorts that break total population into categories such as people of driving age, those of retirement age, and Millennials. This was also investigated for the WisDOT model, but was found not to be better than total population in explaining VMT change. Other potential improvements could address the concerns regarding preferences, urbanization, and saturation. Survey data, the percent of the population living in urban areas (and their characteristics), and vehicle ownership data could be helpful in these areas. As discussed above, there are several complementary components of the economy that could be substituted or added as well. During the development process for the WisDOT model, retail sales and income were studied along with employment, but were found not to be significant in this case. The WisDOT model is meant to be a tool to investigate elasticities more than to produce a hard VMT forecast value. It is one of many tools that together can generate a forecast based on a reasonable and known set of assumptions. While application of the WisDOT model ultimately results in a future forecasted value, this value alone and out of context can be dangerous. The WisDOT model and its results do not themselves speak to whether VMT has permanently leveled off, peaked, or bottomed out. What they say is that given the patterns of the past few decades, if the forecasts for the independent variables are accurate, it is likely that VMT will grow. If there is a tremendous shift from those historic patterns, the WisDOT model should be re-specified to adjust for it. A conditional model was determined to be the most useful for WisDOT, given that the independent variables are all forecasted by multiple legitimate sources and are already being used by WisDOT but if the forecasts themselves end up being inaccurate, the WisDOT model may still perform perfectly well but the original forecast will also be inaccurate a model is only as good as the input data.

10 Mike Sillence The guidance sought, and gratefully received, from both the academic and professional spheres, and the help of the knowledgeable experts in the fields of econometrics and transportation analysis throughout the modeling process was essential and appreciated. Thank you to those who assisted in the creation of this paper. REFERENCES () Transportation Planning Manual, Chapter, Section 0 Data elements of Roadway Traffic Forecasting. Wisconsin Department of Transportation, 0. Accessed March, 0. () March 0 Monthly Energy Review, Chapter Energy Prices. U.S. Department of Energy, Energy Information Administration, 0. Accessed March, 0. T0.0 () Consumer Price Index for All Urban Consumers: Transportation. Bureau of Labor Statistics, U.S. Department of Labor, 0. Accessed June, 0. Servlet () Wisconsin Population. Wisconsin Department of Health Services, 0. Accessed March, 0. () GDP & Personal Income, Regional Data. Bureau of Economic Analysis, U.S. Department of Commerce 0. Accessed April, 0. step=&isuri=&acrdn=#reqid=0&step=&isuri= () The 0-Year Focus First Quarter, Supplementary Data. IHS Global Insight, February 0. () State Population Projections, 0-00, Wisconsin Population & Household Projections. Demographic Services Center, Wisconsin Department of Administration, 0. Accessed May, 0. Services-Center/Wisconsin-Population-Projections/ () 0 State Profile Wisconsin. Woods & Poole Economics Inc., Washington D.C. () IF. Light-duty vehicle energy demand: demographics and travel behavior, Annual Energy Outlook 0. Accessed May, 0. () Highway Sensitivity Analysis, Chapter Sensitivity Analysis, 0 Status of the Nation s Highways, Bridges, and Transit: Conditions & Performance. Federal Highway Administration, U.S. Department of Transportation. Accessed June, cpr/chap.htm#body () Vehicle-miles of travel, by functional system, Section, Highway Statistics 0, Highway Statistics Series. Office of Highway Policy Information, Federal Highway Administration, U.S. Department of Transportation. Accessed June, 0. information/statistics/0/

11 Mike Sillence () Lin, C.-Y. & Price, Lea. Gasoline price volatility and the elasticity of demand for gasoline. Department of Agricultural and Resource Economics, University of California, Davis, California. Accessed May, 0. () Vehicle Miles Traveled (VMT) Statewide Forecast Model. Washington State Department of Transportation Economic Analysis. April 0. Accessed March, 0. AABAFE/0/VMTForecastWorkGroupSummaryMay0final.pdf () Litman, Todd. Understanding Transport Demands and Elasticities. Victoria Transport Policy Institute, Victoria, British Columbia. March 0. Accessed May, 0. () Garceau, T., Atkinson-Palombo, C., & Garrick, N. Peak Travel and the Decoupling of Vehicle Travel from the Economy: A Synthesis of the Literature. Presented at the rd Annual Meeting of the Transportation Research Board, Washington D.C., 0. () Population Estimates, Pre-0 State Tables. U.S. Census Bureau, 0. Accessed March, 0.