Traffic Forecasting Accuracy Assessment Research (2020)

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III-H-1 A P P E N D I X H Deep Dives Contents III-H-3 1 Eastown Road Extension, Lima, Ohio III-H-3 1.1 Introduction III-H-3 1.2 Project Description III-H-5 1.3 Predicted–Actual Comparison of Traffic Forecasts III-H-6 1.4 Potential Sources of Forecast Error III-H-7 1.5 Sources Contributing to Forecast Error III-H-14 1.6 Discussion III-H-14 2 Indian Street Bridge, Palm City, Florida III-H-14 2.1 Introduction III-H-15 2.2 Project Description III-H-16 2.3 Predicted–Actual Comparison of Traffic Forecasts III-H-19 2.4 Potential Sources of Forecast Error III-H-21 2.5 Sources Contributing to Forecast Error III-H-26 2.6 Discussion III-H-29 3 Central Artery Tunnel, Boston, Massachusetts III-H-29 3.1 Introduction III-H-29 3.2 Project Description III-H-30 3.3 Predicted–Actual Comparison of Traffic Forecasts III-H-32 3.4 Potential Sources of Forecast Error III-H-33 3.5 Sources Contributing to Forecast Error III-H-38 3.6 Discussion III-H-38 4 Cynthiana Bypass, Cynthiana, Kentucky III-H-38 4.1 Introduction III-H-39 4.2 Project Description III-H-40 4.3 Predicted–Actual Comparison of Traffic Forecasts III-H-44 4.4 Potential Sources of Forecast Error III-H-45 4.5 Sources Contributing to Forecast Error III-H-48 4.6 Discussion III-H-49 5 South Bay Expressway, San Diego, California III-H-49 5.1 Introduction III-H-50 5.2 Project Description III-H-50 5.3 Traffic Forecasts Method III-H-51 5.4 Potential Sources of Forecast Error III-H-57 5.5 Discussion

III-H-2 Traffic Forecasting Accuracy Assessment Research III-H-58 6 US-41, Brown County, Wisconsin III-H-58 6.1 Introduction III-H-58 6.2 Project Description III-H-61 6.3 Predicted–Actual Comparison of Traffic Forecasts III-H-63 6.4 Potential Sources of Forecast Error III-H-64 6.5 Sources Contributing to Forecast Error III-H-65 6.6 Discussion III-H-67 References

Appendix H: Deep Dives III-H-3 1 Eastown Road Extension, Lima, Ohio 1.1 Introduction The Eastown Road Extension was a project in the city of Lima, Ohio that widened a 2.5-mile segment of the arterial from 2 lanes to 5 lanes and extended the arterial an additional mile. This north- south arterial is located on the western edge of the city of Lima in Allen County, Ohio. The report on this deep dive was written in June 2018. It assesses the reliability and accuracy of traffic forecasts for the Eastown Road Extension project. Traffic forecasts for the project were prepared around 2000 for a 2009 opening year. The project opened around 2009. Traffic counts are available for 2010–2017 years, all post-opening. Section 1.2 describes the project. Section 1.3 compares the predicted and actual traffic volumes for all roadways in the study area where post-opening traffic counts are available. Section 1.4 enumerates the exogenous forecasts and sources of forecast error for the project. It also includes an assessment of the accuracy of the exogenous forecasts. Section 1.5 attempts to identify items discussed in Section 1.4 that are important sources of forecast error and, if so, attempt to quantify how much it would change the forecast if the forecasters had accurate information about the item. Section 1.6 summarizes the findings. 1.2 Project Description The project extended Eastown Road from just north of Elida Road in the north to Spencerville Road in the south. The project included a 2.5-mile expansion from 2 lanes to 5 lanes on the segment between Elida Road and West Elm Street and a 1-mile extension further south to Spencerville Road. Documentation of the project forecasts was unavailable at the time of writing this report. Hence, aspects related to project costs, exact opening year, importance of the project to the local community, and other characteristics could not be determined. Using historical Google Earth images, the opening year was identified to be the year 2009. Figure III-H-1 (next page) shows the project corridor.

III-H-4 Traffic Forecasting Accuracy Assessment Research Source: Map data: Google Earth, annotated by NCHRP 08-110 project team Figure III-H-1. Project corridor for Eastown Road Extension. 5 6 8 9 10 11 W Elm St 1 2 3 4 7 These are the 11 segments identified for traffic volume accuracy assessment

Appendix H: Deep Dives III-H-5 1.3 Predicted–Actual Comparison of Traffic Forecasts The Ohio Department of Transportation (Ohio DOT) made travel demand model runs available for this effort. These model runs have been used to report the predicted traffic on the project. The travel demand model area included the entire Lima Metropolitan Organization (MPO) region. The model is a traditional four-step model in CUBE-Voyager software that includes trip generation, trip distribution, mode choice, and traffic assignment steps. Additional components include a household model that develops the distributions required by the trip generation, and separate truck and external models. The model has a base year of 2000 and the modeled opening year for the Eastown project is 2009. The model runs included loaded highway networks for both the base and opening years. At the time of writing this report, CUBE v6.4.3 was the latest available version, and the model runs were presumably made using earlier versions of CUBE. For the opening-year forecasts to be consistent with the additional model runs made to quantify sources of forecasting error as described in Section 1.5, the opening-year scenario run was remade using CUBE v6.4.2. The loaded network thus generated from this new model run was used to report the link level opening-year forecasts. It should be noted that the differences in the model volumes were very small (less than 2%) between this new model run and the original model run provided by the Ohio DOT. A total of 11 links with an ADT count available were identified in the project corridor. Table III-H-1 lists each of these links with their forecast and observed ADT. The table includes an inaccuracy index in traffic forecasts that was estimated as: = − The first six segments constitute the Eastown Road Extension project. These links are also identified in Figure III-H-1. Table III-H-1. Traffic forecast accuracy—Eastown Road Extension, Lima, Ohio. Seg# Project Segment and Direction Opening- Year Count Opening- Year Forecast (2009) Percent Difference from Forecast Count Year Used 1 North Eastown Road: North of Elida Road 8,474 10,262 -17% 2010 2 North Eastown Road: South of Elida Road 15,071 19,435 -22% 2017 3 North Eastown Road: North of Allentown Road 12,169 16,755 -27% 2010 4 North Eastown Road: South of Allentown Road 15,404 19,099 -19% 2010 5 South Eastown Road: North of Elm Street 15,219 17,181 -11% 2017 6 South Eastown Road: South of Elm Street 8,515 14,907 -43% 2010 7 Allentown Rd: West of Eastown Road 9,740 8,773 11% 2011 8 Elm St: West of Eastown Road 6,314 5,021 26% 2010 9 Elm St: East of Eastown Road 7,793 9,084 -14% 2010 10 Spencerville Rd: Far West of Eastown Rd 8,346 8,882 -6% 2011 11 Spencerville Rd: Far East of Eastown Rd 8,210 10,604 -23% 2011 (III-H-1)

III-H-6 Traffic Forecasting Accuracy Assessment Research The opening-year count data was available from the Ohio DOT’s traffic website. Count data was available for either 2010 or 2011 for most segments except for Segments 2 and 5, for which only a 2017 count was available. Overall, the actual volumes were 20% lower than the forecast on those segments of Eastown Road that were expanded from 2 lanes to 5 lanes. On the new extension segment south of Elm Street, the actual volume was 43% lower than the forecast. This could reflect a potential error in the count data at that location because there’s a 44% reduction in observed traffic counts on Eastown Road between the area north of Elm Street and the area south of Elm Street that cannot be explained by the housing and commercial activities near the intersection. Further, the traffic forecasts on three of the four legs (Segments 5, 8, and 9) at this intersection differ by only about 10–26% from the traffic counts. 1.4 Potential Sources of Forecast Error This section identifies the exogenous forecasts and project assumptions used in the development of the traffic forecasts. Exogenous forecasts are made outside of the immediate traffic forecasting process. Project assumptions are established during project development and serve as the basis for the traffic forecast. Exogenous forecasts and project assumptions are leading sources of forecast error. Population and employment forecasts are examples of exogenous forecasts, and they are commonly identified as a major source of traffic forecasting error. These forecasts are usually made by outside planning agencies, and they are produced on a regular basis; that is, they are not prepared for any individual project. During project development, these forecasts are revised to match the specific assumptions documented by the project team. As a result, population and employment forecasts are exogenous forecasts that involve project-based assumptions. Past forecasting research has identified several exogenous forecasts and project assumptions as common sources of forecast error, including: Macroeconomic conditions (of the region or study area), Population and employment forecasts, Significant changes in land use, Auto fuel prices, Tolling pricing, sensitivity and price levels, Auto ownership, Changes in technology, Travel times within the study area, and Duration between year forecast produced and opening year. Table III-H-2 lists all exogenous forecasts and project assumptions for which observed data was available for the Eastown Road Extension project.

Appendix H: Deep Dives III-H-7 Table III-H-2. Input accuracy assessment table (Eastown Road Extension). Items Quantifiable Observed Opening-Year Values Estimated Opening-Year Values Percent Difference from Forecast Employment * Yes 38,801 48,312 -20% Population ** Yes 78,576 80,854 -3% Car Ownership ** Yes 54,603 56,084 -3% Fuel Price *** Yes $2.34 $1.82 29% Travel Speed I Yes 47 mph 54 mph -13% Macroeconomic Conditions No Data Sources: * https://www.bls.gov/ ** American Community Survey *** https://www.eia.gov/ I The travel speed mentioned in this table is specifically for the off-peak period. Observed value is obtained from https://www.dot.state.oh.us/Divisions/Planning/TechServ/traffic/Pages/TMMS.aspx Table III-H-2 lists all the items that were identified as potential sources of forecasting error and specifically identifies those sources that are important to the Eastown Road Extension project. Observed values for all the factors mentioned in the table are for the year 2009, to be consistent with the observed opening year. The 2009 project-opening year coincided with one of the worst economic downturns in the United States. The downturn resulted in significant unemployment throughout the country and, as can be seen from the table, the actual employment around that period was 20% lower than the employment forecasts used for this project. The year 2008–2009 also coincided with a time when fuel prices were at their peak. After adjusting the fuel prices to the opening year (2009) for inflation, the actual fuel price was 29% higher than the estimated fuel price for the opening year. Travel speed on certain segments of Eastown Road was another key input that had an error, with the observed travel speeds being 13% lower than the modeled speeds. The modeled travel speeds mentioned in this table are specifically for the off-peak period. The modeled peak period travel speeds were similar to the observed speeds. Population and car-ownership forecasts were also quantified, but these were very similar to the observed data in the year 2009. It should be noted that car ownership was exogenous to the forecast. None of the other potential sources of forecasting error identified in the table were deemed to be important in the forecasts for this project. 1.5 Sources Contributing to Forecast Error Building upon the items discussed in Section 1.4 of this appendix, this section attempts to identify items that are important sources of forecast error and, for each item so identified, attempts to quantify how much it would change the forecast if the forecasters had accurate information about each item. Adjusted forecasts for the critical roadways were computed by applying an elasticity to the relative change between the actual and predicted values for each item listed in Section 1.4. Only those items that could be quantified and deemed important for this project were adjusted.

III-H-8 Traffic Forecasting Accuracy Assessment Research The effect on the forecast can be quantified in this way: First, the following equation is used to calculate the change in forecast value, a delta between the opening-year forecast and the actual observed traffic count in the opening year. Change in Forecast Value = ( − ) ( )⁄ Second, a factor of the effect on forecast is calculated by exponentiating an elasticity of the common source errors, and a natural-log of the change rate in forecast value is calculated. This factor is applied to the actual forecast volume to generate an adjusted forecast. The forecasts are adjusted individually for each variable; the adjusted forecast from one row carries over to the next as an input, thereby presenting a cumulative effect of adjustment. Effect on Forecast = ( ∗ln (1+ ℎ )) − 1 Adjusted Forecast = (1 + ) ∗ This deep dive analysis adopted the best elasticity values possible based on those identified by Ewing et al. (2014) via their cross-sectional and longitudinal models and from other transportation literature (Dong et al. 2012; Dunkerley, Rohr, and Daly 2014). It is important to note that the elasticity values identified by Ewing et al. (2014) relate to vehicle-miles traveled (VMT), not traffic volumes. We were not able to find elasticity values specifically for traffic volumes with respect to employment, population, and fuel price. Further, we were not able to find the elasticity value of VMT or traffic volume with respect to employment. To this end, the elasticity values in NCHRP 08-110 reflect two assumptions: (1) the elasticity values of VMT with respect to population and fuel price are close to the elasticity values of traffic volumes given a high correlation between VMT and traffic volumes, and (2) the elasticity values regarding employment are close to the values for per capita income, again because of their high correlation. Table III-H-3 presents the results of quantifying these effects on the forecast. (III-H-2) (III-H-4) (III-H-3)

Table III-H-3. Forecast adjustment table based on elasticities for all segments (Eastown Road Extension). Seg# Items Actual Value Forecast Value Change Required in Forecast Value Elasticity Effect on Forecast (Equation 15) Starting Forecast Volume Adj. Forecast Volume (Equation 16) Remaining Percent Difference Given Adj. Forecast 1 Employment 38,801 48,312 -20% 0.30 -6% 10,262 9,609 -12% 1 Population/Household 78,576 80,854 -3% 0.75 -2% 9,609 9,405 -10% 1 Car Ownership 54,603 56,084 -3% 0.30 -1% 9,405 9,330 -9% 1 Fuel Price/Efficiency $2.340 $1.820 29% (0.20) -5% 9,330 8,872 -4% 1 Travel Time/Speed - - 0% (0.60) 0% 8,872 8,872 -4% 1 Original Traffic Forecast 8,474 10,262 -17% N/A N/A 1 Adjusted Traffic Forecast N/A N/A N/A 10,262 8,872 -4% 2 Employment 38,801 48,312 -20% 0.30 -6% 19,435 18,198 -17% 2 Population/Household 78,576 80,854 -3% 0.75 -2% 18,198 17,812 -15% 2 Car Ownership 54,603 56,084 -3% 0.30 -1% 17,812 17,670 -15% 2 Fuel Price/Efficiency $2.340 $1.820 29% (0.20) -5% 17,670 16,803 -10% 2 Travel Time/Speed - - 0% (0.60) 0% 16,803 16,803 -10% 2 Original Traffic Forecast 15,071 19,435 -22% N/A N/A 2 Adjusted Traffic Forecast N/A N/A N/A 19,435 16,803 -10% 3 Employment 38,801 48,312 -20% 0.30 -6% 16,755 15,688 -22% 3 Population/Household 78,576 80,854 -3% 0.75 -2% 15,688 15,356 -21% 3 Car Ownership 54,603 56,084 -3% 0.30 -1% 15,356 15,233 -20% 3 Fuel Price/Efficiency $2.340 $1.820 29% (0.20) -5% 15,233 14,486 -16% 3 Travel Time/Speed - - 0% (0.60) 0% 14,486 14,486 -16% 3 Original Traffic Forecast 12,169 16,755 -27% N/A N/A 3 Adjusted Traffic Forecast N/A N/A N/A 16,755 14,486 -16% 4 Employment 38,801 48,312 -20% 0.30 -6% 19,099 17,883 -14% 4 Population/Household 78,576 80,854 -3% 0.75 -2% 17,883 17,504 -12% 4 Car Ownership 54,603 56,084 -3% 0.30 -1% 17,504 17,364 -11% 4 Fuel Price/Efficiency $2.340 $1.820 29% (0.20) -5% 17,364 16,513 -7% 4 Travel Time/Speed 0.59 0.49 20% (0.60) -11% 16,513 14,772 4% (continued on next page)

Seg# Items Actual Value Forecast Value Change Required in Forecast Value Elasticity Effect on Forecast Starting Forecast Volume Adj. Forecast Volume Remaining Percent Difference Given Adj. Forecast 4 Original Traffic Forecast 15,404 19,099 -19% N/A N/A 4 Adjusted Traffic Forecast N/A N/A N/A 19,099 14,772 4% 5 Employment 38,801 48,312 -20% 0.30 -6% 17,181 16,087 -5% 5 Population/Household 78,576 80,854 -3% 0.75 -2% 16,087 15,746 -3% 5 Car Ownership 54,603 56,084 -3% 0.30 -1% 15,746 15,620 -3% 5 Fuel Price/Efficiency $2.340 $1.820 29% (0.20) -5% 15,620 14,855 2% 5 Travel Time/Speed 0.83 0.72 15% (0.60) -8% 14,855 13,640 12% 5 Original Traffic Forecast 15,219 17,181 -11% N/A N/A 5 Adjusted Traffic Forecast N/A N/A N/A 17,181 13,640 12% 6 Employment 38,801 48,312 -20% 0.30 -6% 14,907 13,958 -39% 6 Population/Household 78,576 80,854 -3% 0.75 -2% 13,958 13,662 -38% 6 Car Ownership 54,603 56,084 -3% 0.30 -1% 13,662 13,553 -37% 6 Fuel Price/Efficiency $2.340 $1.820 29% (0.20) -5% 13,553 12,888 -34% 6 Travel Time/Speed 1.28 1.11 15% (0.60) -8% 12,888 11,832 -28% 6 Original Traffic Forecast 8,515 14,907 -43% N/A N/A 6 Adjusted Traffic Forecast N/A N/A N/A 14,907 11,832 -28% 7 Employment 38,801 48,312 -20% 0.30 -6% 8,773 8,215 19% 7 Population/Household 78,576 80,854 -3% 0.75 -2% 8,215 8,040 21% 7 Car Ownership 54,603 56,084 -3% 0.30 -1% 8,040 7,976 22% 7 Fuel Price/Efficiency $2.340 $1.820 29% (0.20) -5% 7,976 7,585 28% 7 Travel Time/Speed - - 0% (0.60) 0% 7,585 7,585 28% 7 Original Traffic Forecast 9,740 8,773 11% N/A N/A 7 Adjusted Traffic Forecast N/A N/A N/A 8,773 7,585 28% Table III-H-3 (Continued).

Seg# Items Actual Value Forecast Value Change Required in Forecast Value Elasticity Effect on Forecast Starting Forecast Volume Adj. Forecast Volume Remaining Percent Difference Given Adj. Forecast 8 Employment 38,801 48,312 -20% 0.30 -6% 5,021 4,701 34% 8 Population/Household 78,576 80,854 -3% 0.75 -2% 4,701 4,602 37% 8 Car Ownership 54,603 56,084 -3% 0.30 -1% 4,602 4,565 38% 8 Fuel Price/Efficiency $2.340 $1.820 29% (0.20) -5% 4,565 4,341 45% 8 Travel Time/Speed - - 0% (0.60) 0% 4,341 4,341 45% 8 Original Traffic Forecast 6,314 5,021 26% N/A N/A 8 Adjusted Traffic Forecast N/A N/A N/A 5,021 4,341 45% 9 Employment 38,801 48,312 -20% 0.30 -6% 9,084 8,506 -8% 9 Population/Household 78,576 80,854 -3% 0.75 -2% 8,506 8,325 -6% 9 Car Ownership 54,603 56,084 -3% 0.30 -1% 8,325 8,259 -6% 9 Fuel Price/Efficiency $2.340 $1.820 29% (0.20) -5% 8,259 7,854 -1% 9 Travel Time/Speed - - 0% (0.60) 0% 7,854 7,854 -1% 9 Original Traffic Forecast 7,793 9,084 -14% N/A N/A 9 Adjusted Traffic Forecast N/A N/A N/A 9,084 7,854 -1% 10 Employment 38,801 48,312 -20% 0.30 -6% 8,882 8,317 0.3% 10 Population/Household 78,576 80,854 -3% 0.75 -2% 8,317 8,140 3% 10 Car Ownership 54,603 56,084 -3% 0.30 -1% 8,140 8,075 3% 10 Fuel Price/Efficiency $2.340 $1.820 29% (0.20) -5% 8,075 7,679 9% 10 Travel Time/Speed - - 0% (0.60) 0% 7,679 7,679 9% 10 Original Traffic Forecast 8,346 8,882 -6% N/A N/A 10 Adjusted Traffic Forecast N/A N/A N/A 8,882 7,679 9% 11 Employment 38,801 48,312 -20% 0.30 -6% 10,604 9,929 -17% 11 Population/Household 78,576 80,854 -3% 0.75 -2% 9,929 9,718 -16% 11 Car Ownership 54,603 56,084 -3% 0.30 -1% 9,718 9,641 -15% 11 Fuel Price/Efficiency $2.340 $1.820 29% (0.20) -5% 9,641 9,168 -10% 11 Travel Time/Speed - - 0% (0.60) 0% 9,168 9,168 -10% (continued on next page)

Seg# Items Actual Value Forecast Value Change Required in Forecast Value Elasticity Effect on Forecast Starting Forecast Volume Adj. Forecast Volume Remaining Percent Difference Given Adj. Forecast 11 Original Traffic Forecast 8,210 10,604 -23% N/A N/A 11 Adjusted Traffic Forecast N/A N/A N/A 10,604 9,168 -10% New Extension Employment 38,801 48,312 -20% 0.30 14,907 13,958 -39% Population/Household 78,576 80,854 -3% 0.75 13,958 13,662 -38% Car Ownership 54,603 56,084 -3% 0.30 13,662 13,553 -37% Fuel Price/Efficiency $2.340 $1.820 29% (0.20) 13,553 12,888 -34% Travel Time/Speed 1.28 1.11 15% (0.60) 12,888 11,832 -28% Original Traffic Forecast 8,515 14,907 -43% N/A N/A - - Adjusted Traffic Forecast N/A N/A N/A - - 14,907 11,832 -28% Modified Existing Links 1 Employment 38,801 48,312 -20% 0.30 82,732 77,466 -14% Population/Household 78,576 80,854 -3% 0.75 77,466 75,823 -13% Car Ownership 54,603 56,084 -3% 0.30 75,823 75,217 -12% Fuel Price/Efficiency $2.340 $1.820 29% (0.20) 75,217 71,530 -7% Travel Time/Speed - - 0% (0.60) 71,530 68,574 -3% Original Traffic Forecast 66,337 82,732 -20% N/A N/A Adjusted Traffic Forecast N/A N/A N/A 82,732 68,574 -3% Other Links 2 Employment 38,801 48,312 -20% 0.30 42,364 39,667 2% Population/Household 78,576 80,854 -3% 0.75 39,667 38,826 4% Car Ownership 54,603 56,084 -3% 0.30 38,826 38,516 5% Fuel Price/Efficiency $2.340 $1.820 29% (0.20) 38,516 36,628 10% Travel Time/Speed - - 0% (0.60) 36,628 36,628 Original Traffic Forecast 40,403 42,364 -5% N/A N/A Adjusted Traffic Forecast N/A N/A N/A 42,364 36,628 10% 1 Links which existed before and were modified as part of the project 2 Links which were not part of the project, but for which the traffic was forecast Table III-H-3 (Continued).

Appendix H: Deep Dives III-H-13 The original forecast value was successively adjusted for each of the items identified as contributing sources of forecasting error and the final remaining percentage differences from forecast after all adjustments are shown in the table. Table III-H-3 shows the detailed elasticity-based adjustments made for all the segments. The most significant impact on traffic volumes was due to employment and travel time corrections. Using these elasticity-based adjustments, the percent difference from forecast was improved on all the segments of Eastown Road (segments 1–6), though the actual volume on the portion of Eastown Road which was extended (Segment 6) is still low by 28% in comparison to the adjusted forecast. Segment 6 has a potential error in the count data (as described in Section 1.3). In addition to the elasticity-based adjustment, the travel model used to produce the traffic forecasts was rerun using corrected exogenous forecasts and project assumptions. The same items identified in Section 1.4 were adjusted sequentially in the model. Employment, population and car ownership were uniformly scaled down at the traffic analysis zone (TAZ) level in the model to match the observed values. For fuel price, the auto operating cost was changed in the model. According to a 2013 report on driving costs from the American Automobile Association (2013), approximately 20% of the auto operating cost is due to fuel price. Only this 20% portion of the auto operating cost in the model was adjusted to reflect the observed fuel price. The travel speeds on the corridor-specific segments were changed wherever they were different from the observed values. The model adjustments were performed sequentially to obtain new model volumes. The results of this process for all segments are shown in Table III-H-4. Table III-H-4. Adjusted forecast table using the model (Eastown Road Extension). Seg# Items Old Model Volume New Model Volume Observed Volume Difference (Observed - New) Percent Difference from Observed Volume Percent Difference from Old Model Volume 1 All Adjustments 10,262 9,375 8,474 -901 -10% -9% 2 19,435 16,810 15,071 -1,739 -10% -14% 3 16,755 14,148 12,169 -1,979 -14% -16% 4 19,099 15,337 15,404 67 0% -20% 5 17,181 13,679 15,219 1,540 11% -20% 6 14,907 12,486 8,515 -3,971 -32% -16% 7 8,773 8,249 9,740 1,491 18% -6% 8 5,021 4,601 6,314 1,713 37% -8% 9 9,084 8,999 7,793 -1,206 -13% -1% 10 8,882 8,413 8,346 -67 -1% -5% 11 10,604 9,491 8,210 -1,281 -13% -10% New Extension 14,907 12,486 8,515 -3,971 -32% -16% Modified Existing Links 1 82,732 69,349 66,337 -3,012 -4% -16% Other Links 2 42,364 39,753 40,403 650 2% -6% 1 Links which existed before and were modified as part of the project. 2 Links which were not part of the project, but for which the traffic was forecast.

III-H-14 Traffic Forecasting Accuracy Assessment Research Overall, the final adjusted forecasts using the model were very similar to those obtained from the elasticity-based adjustments, especially on Eastown Road (Segments 1–6). 1.6 Discussion The actual volumes on Eastown Road were 20% lower than those forecast for the existing portion of the road and 43% lower than those forecast for the extension. It should be noted that there is a possible error in the observed counts on the extension segment. The project opened in 2009, which was the time of peak economic recession and high gas prices in the country. As a result, overestimation of employment and underestimation of fuel price in the opening year were two key contributors to the forecasting errors in this project. Additionally, the observed travel speeds on certain segments of the project were 13% lower than the modeled speed. This was the third key contributor to the forecasting error in this project. Population and car ownership forecasts were very similar to the observed values and contributed a tiny portion to the forecasting error. Adjustments to the forecasts using elasticities and model reruns confirmed that significant errors in opening-year forecasts of employment, fuel price, and travel speed had a major role in the overestimation of traffic volumes on Eastown Road. The traffic forecasts on the project segments that were widened from 2 lanes to 5 lanes improved, and the actual volumes were now only 3% lower than the adjusted forecasts after accounting for the corrected exogenous forecasts and project assumptions. The forecasts on the extension segment improved as well, with the actual volumes now 28% lower than the adjusted forecasts. Overall, the prevailing macroeconomic conditions around the opening year played a major part in the accuracy of the forecasts for the Eastown Road Extension project. This is a major uncertainty that is extremely difficult to directly consider at the time of preparation of traffic forecasts given the various modeling parameters that could change in an economic downturn. One way to account for this is to evaluate and document the change in traffic forecasts using reduced employment and higher fuel prices. It is unknown whether risk and uncertainty were considered in the traffic forecasts for this project due to the absence of project documentation. For future forecasting efforts, it is suggested that a copy of the project and traffic forecasting documentation be saved along with the actual model used to generate the forecasts. 2 Indian Street Bridge, Palm City, Florida 2.1 Introduction The Indian Street Bridge is a new bridge construction project located in Palm City, Florida (Martin County). The bridge is 0.6 miles long with 4 travel lanes in total (2 lanes in each direction). This bridge runs along CR-714 (Martin Highway), connecting with Indian Street and going across the St. Lucie River. This report, written in June 2018, assesses the reliability and accuracy of traffic forecasts for the Indian Street Bridge project. Traffic forecasts for the project were reported in 2003 for the 2011, 2021, and 2031 forecast years. The project was scheduled to be opened in 2011, but the project opened in 2014. The ADT counts are available for the year 2014.

Appendix H: Deep Dives III-H-15 Section 2.2 describes the project. Section 2.3 compares the predicted and actual traffic volumes for all roadways in the study area for which post-opening traffic counts are available. Section 2.4 enumerates the exogenous forecasts and sources of forecast error for the project. It also includes an assessment of the accuracy of the exogenous forecasts. Section 2.5 attempts to identify items discussed in Section 2 that are important sources of forecast error and, for those identified, attempts to quantify how much it would change the forecast if the forecasters had accurate information about the item. Section 2.6 summarizes the findings. 2.2 Project Description The Indian Street Bridge acts as a reliever bridge for the Palm City Bridge (old bridge), which is approximately 1 mile north of the new bridge. It is also expected to provide relief to the existing SR-714 corridor, which is connected with the Palm City Bridge. The study area boundaries extend from Florida’s Turnpike to the west, Federal Highway (US-1) to the east, the I-95 crossing of the St. Lucie Canal to the south, and the Martin County/St. Lucie County line to the north. This project concentrated on multiple alternatives and later finalized on a new 4-lane bridge construction. The updated study was reported in 2003. The construction was started in 2009 and was completed in 2014. The estimated construction cost of the project was $63.9 million. This project is interesting because it provides an opportunity to examine a new bridge project crossing over a river, with clear diversion effects and detailed modeling information available. The model was built using the Transportation Planning (TRANPLAN) program. Florida DOT’s D4 provided archived model runs and detailed project reports to support this deep dive analysis. Figure III-H-2 (next page) shows the study area for this project.

III-H-16 Traffic Forecasting Accuracy Assessment Research Source: Map data: Indian Street Bridge PD&E, Design Traffic Technical Memorandum, Florida Department of Transportation (January 23, 2003) Figure III-H-2. Project corridor for Indian Street Bridge. 2.3 Predicted–Actual Comparison of Traffic Forecasts The New Bridge Crossing Alternative Corridor Alignment Report (Corridor Report) was completed in March of 2001 and was later updated in 2003. The study involves two major corridors: SR-714, which is also referred to as North Corridor and includes the Palm City Bridge, and CR-714, which is referred to as South Corridor and includes the Indian Street Bridge. On top of new bridge construction, the project also includes upgrading of CR-714 from a 3-lane rural section with a center 2- way left-turn lane to a 4-lane arterial and other minor improvements to the side streets at signalized intersections. There are 47 links in the study area. Traffic forecasts were obtained for 25 of the 45 links. The Treasure Coast Regional Planning Model (TCRPM II) 2025 Cost Feasible Model (A25) was used in the evaluation of this project. The base year for this model was 1996 and the horizon year was 2025. The 2001, 2011, 2021, and 2031 traffic volumes for the corridors in this study area were calculated using a combination of linear regression, turning movement procedures, four-step travel demand model forecasting, and professional judgment. The study incorporated model estimates and historical traffic information, where appropriate. Historical traffic count data was obtained between 1992 to 2001 for selected county and Florida DOT count stations that were in the project study area. On a few corridors, where the estimated traffic was unexplainable, professional judgment was used to adjust the growth rate

Appendix H: Deep Dives III-H-17 and to reassign the design-year traffic volumes. The detailed approach can be found in the Revised Traffic Projection and Turning Movement Report for SR-714 and Martin Highway/Indian Street (Indian Street Bridge Crossing). There are 25 links with an ADT traffic count. For this analysis, we have only concentrated on 11 links near the two bridges. Links 1-3 are on the South Corridor and Links 4-7 are on the North Corridor. The rest of the links are cross-sectional links connecting the north and south corridors. Figure III-H-3 shows the locations of all the 11 links analyzed in this report. Segment 1 is the new Indian Street Bridge, and Segment 5 refers to the existing Palm City Bridge. Source: Map data: Google Maps, annotated by NCHRP 08-110 project team Figure III-H-3. Project corridors and important links (Indian Street project). Table III-H-5 lists each of these links with base-year traffic count and their forecast and observed ADT in the opening year. The table adds an inaccuracy index in traffic forecasts that was estimated as: = − The new bridge was proposed to open in 2011, but it actually opened in December 2013. Therefore, the 2014 counts were compared with the 2011 forecast volumes in this exercise. The 2001 counts and 2011 forecast volumes in Table III-H-5 were obtained from the Indian Street Bridge PD&E, and opening-year counts in terms of ADT were assembled from the Martin County 2014 Roadway Level of Service Inventory Report. These are the 11 segments identified for traffic volume accuracy assessment 1 6 5 4 3 2 11 10 9 8 7 (III-H-1)

III-H-18 Traffic Forecasting Accuracy Assessment Research Table III-H-5. Comparison of base-year and opening-year traffic counts and opening-year traffic forecast (Indian Street Bridge project). Site ID Site Segment Report Base Year Opening-Year Count Opening-Year Forecast Percent Difference from Forecast2001 2014 2011 1 South Corridor: CR-714, from St Lucie River to SR-76 [new bridge] Build Project 17,129 42,900 -60% 2 South Corridor: Indian Street from SR-76 to Willoughby Blvd. 14,500 21,866 27,600 -21% 3 South Corridor: CR-714,from west of Mapp Road 9,900 18,213 22,300 -18% 4 North Corridor: SR-714 from SR-76 to Willoughby Blvd. 29,900 23,370 33,000 -29% 5 North Corridor: SR-714 from Mapp Road to Palm City Ave. [old bridge] 48,000 33,675 52,800 -36% 6 North Corridor: SR-714 from Palm City Ave. to SR-76 43,300 33,675 46,400 -27% 7 North Corridor: SR-714,west of Mapp Road 32,300 28,678 34,000 -16% 8 Palm City Ave. north of SR-714 8,800 7,010 9,700 -28% 9 SR-76, north of Indian Street 22,200 21,883 23,900 -8% 10 Willoughby Blvd. from south of SR-714 9,000 9,565 17,800 -46% 11 Mapp Road from north of CR-714 14,600 11,835 17,000 -30% In general, for all the links in the study area, the volumes are overestimated by the model (see Table III-H-5). The percent difference is very high for the main corridors: the new bridge (speed limit of 45 mph) volume estimates exceed the actual counts by 60%, whereas the old bridge (speed limit of 40 mph) actual volumes exceed the estimates by 36%. The forecast volume is also almost double the actual count for the Willoughby Blvd., which connects the SR-714 and CR-714 corridors. Surprisingly, the SR-76 corridor had reasonable opening-year volume estimates compared to the actual counts, given that all the corridors surrounding it had high inaccuracy in estimating the opening- year volumes. After analyzing further for the distribution of volumes from the old and new bridges across the nearby area, it was observed that the SR-76 to the south of SR-714 hardly encountered any flow coming from the old bridge. The same condition was true for Willoughby Blvd., which did not encounter much flow from any of the bridges, and yet its traffic was overestimated by 46%. This might suggest that the overall traffic projected by the model has been overestimated or that the distribution of modeled trips does not match the actual trip distribution. Base-year counts and modeled volumes were compared to ensure that the latter assumption was not the case. Analysis also suggests that most of the trips using the new and old bridges are within Martin County boundary lines. Figure III-H-4 shows the flow of traffic from old and new bridges. The red and black lines show the amount of traffic coming from the new and old bridges, respectively, on neighboring links. Thicker lines indicate higher traffic volumes. The thinnest line represents 1,000 trips.

Appendix H: Deep Dives III-H-19 As seen in the figure, the two bridges in yellow and blue colors have the thickest lines, and the lines become thinner as the traffic is dissipated away from the bridges. Source: Map created by NCHRP 08-110 project team Figure III-H-4. Distribution of traffic from old and new bridges. 2.4 Potential Sources of Forecast Error This section identifies the exogenous forecasts and project assumptions used in the development of the traffic forecasts. Exogenous forecasts are made outside of the immediate traffic forecasting process. Project assumptions are established during project development and serve as the basis for the traffic forecast. Exogenous forecasts and project assumptions are leading sources of forecast error. Population and employment forecasts are examples of exogenous forecasts, and they are commonly identified as a major source of traffic forecasting error. These forecasts are usually made by outside planning agencies, and they are made on a regular basis; that is, they are not prepared for any individual project. During project development, these forecasts are revised to match the specific assumptions documented by the project team. In this example, population, car-ownership, and employment forecasts are all exogenous forecasts and involve project assumptions.

III-H-20 Traffic Forecasting Accuracy Assessment Research Past forecasting research has identified several exogenous forecasts and project assumptions as common sources of forecast error, including: Macroeconomic conditions (of the region or study area), Population and employment forecasts, Significant changes in land use, Auto fuel prices, Tolling pricing, sensitivity and price levels, Auto ownership, Changes in technology, Travel times within the study area, and Duration between year forecast produced and opening year. Table III-H-6 shows a list of all the items that were identified as potential sources of forecasting error and specifically identifies those sources which are important to the Indian Street Bridge project. Observed values for all the factors mentioned in the table are for the year 2014, to be consistent with the observed opening year. The population, employment, and car ownership values are given for all three counties in the model (Indian River, St. Lucie, and Martin counties). The 2011 estimates were calculated by interpolating the 1996 and 2025 socioeconomic data from the model. In terms of population, the St. Lucie County region is currently developing fast. Consequently, even though the populations for Indian River County and Martin County were overestimated (by 1–3%), the total regional population was underestimated by 6% because the population for St. Lucie county was underestimated by 22%. The major economic recession in 2008 occurred between the year the forecast was made and the opening year of the project. This downturn resulted in significant unemployment throughout the United States and as a result, the travel demand model estimate of opening-year employment was 10% higher than the actual employment for the project’s opening year. Table III-H-6. Input accuracy assessment table (Indian Street Bridge project). Items Quantifiable Observed Opening-Year Values (2014) Estimated Opening-Year Values (2011) Percent Difference from Forecast Employment * Yes 177,966 198,138 -10% Population ** Yes 574,564 542,395 6% Car Ownership ** Yes 364,503 337,742 8% Fuel Price *** Yes $ 3.40 $ 1.91 78% Macroeconomic Conditions No Data Source for Observed Value: * https://beta.bls.gov/ ** 2014 American Community Survey Data *** https://www.eia.gov/ The estimated opening-year fuel price is a proxy fuel price determined after adjusting for the inflation between 1996 and 2014 and is specific to the lower Atlantic region of the United States. It is important to note that 2014 was one of the years when the fuel price hike was observed. Although fuel prices were not used directly in the model used to produce Indian Street Bridge forecasts, it should

Appendix H: Deep Dives III-H-21 be noted that even after adjusting for inflation, the fuel prices in the opening year for the Indian Street Bridge were underestimated by 78%. Of the other potential sources of forecasting error identified in the table, none was deemed to be important in the forecasts for this project. 2.5 Sources Contributing to Forecast Error Building upon the items discussed in Section 2.4, this section attempts to identify items that were important sources of forecast error and, for the items so identified, attempts to quantify how much it would change the forecast if the forecasters had accurate information about the item. Adjusted forecasts for the critical roadways were computed by applying an elasticity formula to the relative change between the actual and predicted values for each item in Section 2.4. Only those items that could be quantified and deemed important for this project were adjusted. The effect on the forecast can be quantified in this way: First, the following equation is used to calculate the change in forecast value, a delta between the opening-year forecast and the actual observed traffic count in the opening year. Change in Forecast Value = ( − ) ( )⁄ Second, a factor of the effect on forecast is calculated by exponentiating an elasticity of the common source errors, and a natural-log of the change rate in forecast value is calculated. This factor is applied to the actual forecast volume to generate an adjusted forecast. Effect on Forecast = ( ∗ (1+ ℎ )) − 1 Adjusted Forecast = (1 + ) ∗ This deep dive analysis adopted the best elasticity values possible based on those identified by Ewing et al. (2014) via their cross-sectional and longitudinal models and from other transportation literature (Dong et al. 2012; Dunkerley, Rohr, and Daly 2014). It is important to note that the elasticity values identified by Ewing et al. (2014) relate to VMT, not traffic volumes. We were not able to find elasticity values specifically for traffic volumes with respect to employment, population, and fuel price. Nor were we able to find the elasticity value of VMT, nor traffic volume with respect to employment. To this end, the elasticity study in NCHRP 08-110 reflects two assumptions: (1) the elasticity values of VMT with respect to population and fuel price are close to the elasticity values of traffic volumes given a high correlation between VMT and traffic volumes, and (2) the elasticity values regarding employment are close to the ones for per capita income, again because of their high correlation. The elasticity values used in the Indian Street Bridge study were: 0.75 for population, 0.3 for capita income (employment), and 0.2 for fuel price. The results of quantifying the effect on the forecast are shown in Table III-H-7. (III-H-2) (III-H-3) (III-H-4)

Table III-H-7. Forecast adjustment table based on elasticities for all segments (Indian Street Bridge project). Seg# Items Actual Value Forecast Value Change Required in Forecast Value Elasticity Effect on Forecast Actual Forecast Volume Adj. Forecast Volume Remaining Percent Difference Given Adj. Forecast 1 Employment 177,966 198,138 -10% 0.30 -3% 42,900 41,540 -59% 1 Population 574,564 542,395 6% 0.75 4% 41,540 43,375 -61% 1 Fuel Price 3.40 1.91 78% (0.20) -11% 43,375 38,651 -56% 1 Original Traffic Forecast 17,129 42,900 150% N/A N/A 1 Adjusted Traffic Forecast N/A N/A N/A 42,900 38,651 -56% 2 Employment 177,966 198,138 -10% 0.30 -3% 27,600 26,725 -18% 2 Population 574,564 542,395 6% 0.75 4% 26,725 27,905 -22% 2 Fuel Price 3.40 1.91 78% (0.20) -11% 27,905 24,867 -12% 2 Original Traffic Forecast 21,866 27,600 26% N/A N/A 2 Adjusted Traffic Forecast N/A N/A N/A 27,600 24,867 -12% 3 Employment 177,966 198,138 -10% 0.30 -3% 22,300 21,593 -16% 3 Population 574,564 542,395 6% 0.75 4% 21,593 22,547 -19% 3 Fuel Price 3.40 1.91 78% (0.20) -11% 22,547 20,092 -9% 3 Original Traffic Forecast 18,213 22,300 22% N/A N/A 3 Adjusted Traffic Forecast N/A N/A N/A 22,300 20,092 -9% 4 Employment 177,966 198,138 -10% 0.30 -3% 33,000 31,954 -27% 4 Population 574,564 542,395 6% 0.75 4% 31,954 33,365 -30% 4 Fuel Price 3.40 1.91 78% (0.20) -11% 33,365 29,732 -21% 4 Original Traffic Forecast 23,370 33,000 41% N/A N/A 4 Adjusted Traffic Forecast N/A N/A N/A 33,000 29,732 -21% 5 Employment 177,966 198,138 -10% 0.30 -3% 52,800 51,126 -34% 5 Population 574,564 542,395 6% 0.75 4% 51,126 53,384 -37% 5 Fuel Price 3.40 1.91 78% (0.20) -11% 53,384 47,571 -29% 5 Original Traffic Forecast 33,675 52,800 57% N/A N/A 5 Adjusted Traffic Forecast N/A N/A N/A 52,800 47,571 -29% 6 Employment 177,966 198,138 -10% 0.30 -3% 46,400 44,929 -25% 6 Population 574,564 542,395 6% 0.75 4% 44,929 46,913 -28% (continued on next page)

Table III-H-7 (Continued). Seg# Items Actual Value Forecast Value Change Required in Forecast Value Elasticity Effect on Forecast Actual Forecast Volume Adj. Forecast Volume Remaining Percent Difference Given Adj. Forecast 6 Fuel Price 3.40 1.91 78% (0.20) -11% 46,913 41,805 -19% 6 Original Traffic Forecast 33,675 46,400 38% N/A N/A 6 Adjusted Traffic Forecast N/A N/A N/A 46,400 41,805 -19% 7 Employment 177,966 198,138 -10% 0.30 -3% 34,000 32,922 -13% 7 Population 574,564 542,395 6% 0.75 4% 32,922 34,376 -17% 7 Fuel Price 3.40 1.91 78% (0.20) -11% 34,376 30,633 -6% 7 Original Traffic Forecast 28,678 34,000 19% N/A N/A 7 Adjusted Traffic Forecast N/A N/A N/A 34,000 30,633 -6% 8 Employment 177,966 198,138 -10% 0.30 -3% 9,700 9,393 -25% 8 Population 574,564 542,395 6% 0.75 4% 9,393 9,807 -29% 8 Fuel Price 3.40 1.91 78% (0.20) -11% 9,807 8,739 -20% 8 Original Traffic Forecast 7,010 9,700 38% N/A N/A 8 Adjusted Traffic Forecast N/A N/A N/A 9,700 8,739 -20% 9 Employment 177,966 198,138 -10% 0.30 -3% 23,900 23,142 -5% 9 Population 574,564 542,395 6% 0.75 4% 23,142 24,164 -9% 9 Fuel Price 3.40 1.91 78% (0.20) -11% 24,164 21,533 2% 9 Original Traffic Forecast 21,883 23,900 9% N/A N/A 9 Adjusted Traffic Forecast N/A N/A N/A 23,900 21,533 2% 10 Employment 177,966 198,138 -10% 0.30 -3% 17,800 17,236 -44.5% 10 Population 574,564 542,395 6% 0.75 4% 17,236 17,997 -47% 10 Fuel Price 3.40 1.91 78% (0.20) -11% 17,997 16,037 -40% 10 Original Traffic Forecast 9,565 17,800 86% N/A N/A 10 Adjusted Traffic Forecast N/A N/A N/A 17,800 16,037 -40% 11 Employment 177,966 198,138 -10% 0.30 -3% 17,000 16,461 -28% 11 Population 574,564 542,395 6% 0.75 4% 16,461 17,188 -31% 11 Fuel Price 3.40 1.91 78% (0.20) -11% 17,188 15,316 -23% 11 Original Traffic Forecast 11,835 17,000 44% N/A N/A 11 Adjusted Traffic Forecast N/A N/A N/A 17,000 15,316 -23%

Seg# Items Actual Value Forecast Value Change Required in Forecast Value Elasticity Effect on Forecast Actual Forecast Volume Adj. Forecast Volume Remaining Percent Difference Given Adj. Forecast New Bridge Employment 177,966 198,138 0.30 42,900 41,540 -59% Population 574,564 542,395 0.75 41,540 43,375 -61% Fuel Price 3.40 1.91 (0.20) 43,375 38,651 -56% Original Traffic Forecast 17,129 42,900 150% N/A N/A Adjusted Traffic Forecast N/A N/A N/A 42,900 38,651 -56% Parallel Bridge Employment 177,966 198,138 0.30 52,800 51,126 -34% Population 574,564 542,395 0.75 51,126 53,384 -37% Fuel Price 3.40 1.91 (0.20) 53,384 47,571 -29% Original Traffic Forecast 33,675 52,800 57% N/A N/A Adjusted Traffic Forecast N/A N/A N/A 52,800 47,571 -29% All Other Links Employment 177,966 198,138 0.30 231,700 224,355 -22% Population 574,564 542,395 0.75 224,355 234,263 -25% Fuel Price 3.40 1.91 (0.20) 234,263 208,754 -16% Original Traffic Forecast 176,095 231,700 32% N/A N/A Adjusted Traffic Forecast N/A N/A N/A 231,700 208,754 -16%

Appendix H: Deep Dives III-H-25 The original forecast value was successively adjusted for each of the items (except car-ownership) that had been identified as contributing sources of forecasting error for all the segments. Table III-H-7 shows that the employment rate and fuel price have negatively impacted the volumes on most of the links. The impact due to fuel price is the highest, compared to population and employment. After adjustments for all three important factors, the percentage difference between the adjusted forecast and the observed volumes is reduced by 4 points for the Indian Street Bridge. The percentage difference was reduced by 8% for the Palm City Bridge and by 6% for Willoughby Blvd. The rest of the links showed a 4–10 point improvement in the forecast. As seen from the table, even after these elasticity-based adjustments, most of the links (especially the two competing bridges) show significantly higher forecast volumes than the observed ADT. The percent difference in the adjusted forecast for the Indian Street Bridge is -56% while that for the Palm City Street Bridge is -29%. In addition to the elasticity-based adjustments, the travel demand model was rerun using corrected exogenous forecasts and project assumptions. The TCRPM II (A25) model was provided by the Florida DOT D4 office. The TCRPM-II is a traditional four-step model developed in TRANPLAN that includes trip generation, trip distribution, mode choice, and traffic assignment. For this bridge study, however, the mode choice step was disabled. As a result, the fuel prices could not be adjusted in the new model run. Generally, the fuel prices are used in calculating vehicle operating cost, which in turn is used in the mode choice segment. Thus, for this model, the operating cost was not part of the forecasting method. However, Table III-H-8. 2025 original model run socioeconomic inputs by county. County Persons/Household Autos/Household Persons Autos Martin 2.20 1.71 227,829 176,753 Indian River 2.40 1.58 183,736 121,163 St. Lucie 2.43 1.61 321,931 212,733 The new 2011 link volumes were calculated by scaling down the link volumes from the newly generated 2025 loaded network using the originally reported 2011 and 2025 volume ratio (from the technical memorandum). The resulting 2011 estimates are summarized in Table III-H-9. Overall, the socioeconomic corrections had very little impact on the link volumes for any corridor. The differences in the forecast between the new and the competing bridges were still high (59% and 34%, respectively). Compared to the original forecast, they only decreased by 3 points individually. fuel price might be somewhat accounted for in the model through latent sources accounted for in the model through global model parameters. As a result, the elasticity correction for fuel price was performed as shown in Table III-H-7. The TCRPM II model was converted in the latest available TRANPLAN version. As a result, the base 2025 forecasts were not exactly the same as the 2025 forecasts reported in the agency’s technical memorandum. Moreover, the population and employment numbers were adjusted sequentially to obtain the new model volumes. It was assumed for this analysis that car ownership and persons per household had not changed for any year. Therefore, the number of automobiles and the number of housing units were adjusted in the new run such that the average autos per household and the average persons per household were kept the same as in the original 2025 estimate (see Table III-H-8).

III-H-26 Traffic Forecasting Accuracy Assessment Research Table III-H-9. Adjusted forecast table using the model (Indian Street Bridge project). Seg# Items Old Model Volume New Model Volume Observed Volume Difference (New - Observed) Percent Difference from Observed Volume Percent Difference from Old Model Volume 1 All Adjustments 42,900 41,767 17,129 -24,638 -59% -3% 2 27,600 26,770 21,866 -4,904 -18% -3% 3 22,300 21,479 18,213 -3,266 -15% -4% 4 33,000 32,466 23,370 -9,096 -28% -2% 5 52,800 51,324 33,675 -17,649 -34% -3% 6 46,400 43,959 33,675 -10,284 -23% -5% 7 34,000 32,481 28,678 -3,803 -12% -4% 8 9,700 8,338 7,010 -1,328 -16% -14% 9 23,900 24,022 21,883 -2,139 -9% 1% 10 17,800 17,388 9,565 -7,823 -45% -2% 11 17,000 17,093 11,835 -5,258 -31% 1% New Bridge 42,900 41,767 17,129 -24,638 -59% -3% Parallel Bridge 52,800 51,324 33,675 -17,649 -34% -3% Other links 231,700 223,996 176,095 -47,901 -21% -3% 2.6 Discussion The model forecast on the Indian Street Bridge (new construction) was generally overestimated by about 60%, and on the Palm City Bridge (competing route) the forecast was overestimated by 36%. After applying corrections through elasticity, the percent difference from forecast on the new bridge was reduced to 56%, and on the competing bridge it was reduced to 29%. Model alterations resulted in new forecast volumes that were 59% off for the new bridge and 34% off for the old bridge. Adjustments were made in the model forecast based on the elasticity and the model reruns. The elasticity study showed more promising results as compared to the model adjustments. Fuel price was an influencing factor during the corrections by elasticity. Inclusion of fuel price effects in the model could have been beneficial in reducing error; however, both methods could only explain part of the forecasting error. Clearly, other factors that are not accounted for in the model caused overall underestimation of the traffic in the study area and especially on the Indian Street Bridge.

Appendix H: Deep Dives III-H-27 One source of error might have been the forecasting method. The opening-year traffic was forecast by scaling the design-year model volumes in accordance with existing counts. Because the new bridge had no existing count information, such a procedure might have given rise to inaccurate forecasts; however, it is challenging to develop a more robust forecasting method for projects for which no existing counts are available. Moreover, the bridge may represent too intense a change in infrastructure because it connects two different land areas through a single link, and few comparable alternative paths are available. The effects of an economic downturn might impact the travel behavior of a particular region for years following the recession. For example, Figure III-H-5 shows the clear impact of the 2008 recession on Martin County unemployment. The years 2010 to 2012 show peak unemployment. Job losses affect not only work trips but also leisure trips. Moreover, the recession is assumed to cause a change in the value of time, which also results in an updated coefficient for highway assignment purposes. Changes in job locations, even while maintaining the same housing locations, would alter individuals’ route selections and would clearly change travel patterns for the following years. These effects could be better studied by comparing the information available on trips from Big Data sources (e.g., Streetlight or AirSage data) before and after the recession years. Source: Federal Reserve Economic Data (FRED) database (https://fred.stlouisfed.org) Figure III-H-5. Martin County unemployment rate chart. As seen in Table III-H-10, external trips accounted for 9% of the traffic on the new Indian Street Bridge and only 2% of the traffic on the Palm City Bridge. This information supports the assumption that both the new and the old bridge are heavily used by the internal population. Further analysis comparing the modeled trip patterns to the information available from Big Data sources might reveal travel patterns that were insufficiently represented in the model.

III-H-28 Traffic Forecasting Accuracy Assessment Research Table III-H-10. External trip distribution using both competing bridges. 2025 Original Run 2025 New Run External Trips New Bridge Old Bridge Total New Bridge Old Bridge Total I-95 1,563 - 1,563 1,523 6 1,529 Turnpike 1,227 936 2,163 1,298 968 2,266 US-1 1,095 174 1,269 1,080 286 1,366 Total 3,885 1,110 4,995 3,901 1,260 5,161 Another possible factor is that Martin County, St. Lucie County, and Indian River County show the steepest increase in the median age of the population (see Figure III-H-6). This information suggests that a lot of retirees moved into this region. Retirees tend to travel less than working families do, which may help explain why the population of St. Lucie County was underestimated by 22% while the traffic forecasts Figure III-H-6. Median age (in years) in southeastern Florida counties. Overall, the prevailing macroeconomic conditions around the project’s opening year played a major part in the accuracy of the forecasts. Other exogenous factors causing the overestimate may have been the increase in fuel prices and an increase in retirees. Both of these factors could not be replicated precisely in the travel model used for the Indian Street Bridge. Further analysis using Big Data sources could add more insight on the overestimation of traffic. This study highlights the importance of archiving not only the model runs and forecast reports but also the validation approach used during model development. 20 25 30 35 40 45 50 55 1970 1980 1990 2000 2010 M ed ia n A ge Year Median Age (in years) in Southeast Florida Counties (Source: BEBR) U.S. FLORIDA Indian River Martin Miami-Dade Palm Beach St. Lucie for all links in the study area were overestimated. The travel model did not have a component that adjusted the travel rates based on the number of workers in the household, which may have contributed to the overestimation.

Appendix H: Deep Dives III-H-29 3 Central Artery Tunnel, Boston, Massachusetts 3.1 Introduction The I-93 Central Artery/Tunnel project (CA/T), popularly known as the “Big Dig,” is a megaproject that included the reconstruction of Interstate Highway 93 (I-93) in downtown Boston, Massachusetts; the extension of I-90 to the General Edward Lawrence Logan International Airport (Logan Airport); the construction of two new bridges over the Charles River; six interchanges; and the Rose Kennedy Greenway in the space vacated by the previous elevated I-93 central artery. The project involved 7.8 miles of highway construction, about half of which took place in tunnels. This report, written in May 2018, assesses the accuracy of traffic forecasts for the CA/T project in downtown Boston. This deep dive analysis was prepared based upon the best available resources, including publicly available documents and telephone and email correspondence with local staff. The travel demand model data were not available. This report focuses on 10 roadway links along the central artery corridor and two roadway links of the tunnel to Logan Airport, totaling 12 links. Traffic forecasts were prepared in 1987 for the forecast year 2010. All roadways opened in 2005. Traffic counts are available for 1977, 1987, 1999, 2005, and 2010. This report consists of six sections. Section 3.2 describes the project. Section 3.3 compares the predicted and actual traffic volumes for all roadways in the study area for which post-opening traffic counts were available. Section 3.4 enumerates the exogenous forecasts and sources of forecast error for the project. It also includes an assessment of the accuracy of the exogenous forecasts. Section 3.5 attempts to identify items discussed in Section 3.4 that are important sources of forecast error and, for those items so identified, attempts to quantify how much it would change the forecast if the forecasters had accurate information about the item. Section 3.6 summarizes the findings. 3.2 Project Description The study area for this deep dive consisted of the I-93 in downtown Boston, Massachusetts, and the I-90 near the Ted Williams Tunnel that connects to Logan Airport under Boston Harbor. The 7.8- mile CA/T project included: Replacement of the deteriorating elevated I-93 central artery with an 8- to 10-lane underground expressway, highlighting a pair of 1.5-mile tunnels; Construction of the new 1.6-mile Ted Williams Tunnel to Logan Airport; 3.5-mile extension of I-90 to the Ted Williams Tunnel; Construction of the Leonard P. Zakim Bunker Hill Memorial Bridge and the Leverett Circle Connector Bridge over the Charles River; Construction of six new interchanges; and Construction of the Rose Kennedy Greenway in the space vacated by the previous elevated I-93 central artery. A highlight of the CA/T project was the replacement of the elevated I-93 central artery with the underground expressway. It was built to reduce traffic congestion and improve mobility and environment

III-H-30 Traffic Forecasting Accuracy Assessment Research in one of the most congested parts of Boston and the United States, and to establish the groundwork for economic growth. Figure III-H-7 shows the various CA/T projects. Source: Transportation Impacts of the Massachusetts Turnpike Authority and the Central Artery/Third Harbor Tunnel Project, Economic Development Research Group, Inc. (February 2006) Figure III-H-7. CA/T project locations. 3.3 Predicted–Actual Comparison of Traffic Forecasts There are twelve roadway links in the study area. The twelve links consists of ten roadway segments along the central artery corridor (five for northbound and the other five for southbound) and two links (located in a same location but reflecting a different direction) at the Ted Williams Tunnel. Figure III-H-8 shows the locations of the all links. A few sources, including an original Final Environmental Impact Statement (FEIS) in 1985, a Final Supplemental Environmental Impact Statement (FSEIS) in 1991, and a FEIS for the Charles River Crossing in 1993 provided the mid-year 2010 traffic forecasts (no traffic forecasts for the 2005 opening year were available). Traffic forecasts in the documentation, however, were not consistent. The inconsistency was due in part to the change of a base year, from 1982 in the 1985 FEIS to 1987 in the 1991 FSEIS. The Central Transportation Planning Staff (CTPS), the Boston region’s MPO, conducted a backcasting study in October 2014. This study provided traffic forecasts for the year 2010 (which were

Appendix H: Deep Dives III-H-31 retrieved from the 1991 Final Supplemental Environmental Impact Report [FSEIR]1) and 2010 traffic count data. Base-year traffic counts were retrieved from the CTPS’s Highway Traffic Volumes report.2 The traffic forecasts were outputs of the traffic-only TranPlan model that the CA/T project team had developed for the CA/T study area in the 1980s. Source: Map data, Google Maps, annotated by NCHRP 08-110 project team Figure III-H-8. Traffic count links in the study area (CA/T project). 1 The EIR is the state document whereas the EIS is the federal document. FSEIR/FSEIS were same for the CA/T Project but due to different environmental priorities, the order of document was different; confirmed by email correspondence with Bill Kuttner at CTPS on May 15, 2018. 2 CTPS express highway volumes, I-93/Central Artery Between Columbia Road, Dorchester, and Route 1, Charlestown, ftp://ctps.org/pub/Express_Highway_Volumes/20_I93_Central_Artery.pdf

III-H-32 Traffic Forecasting Accuracy Assessment Research Table III-H-11 lists each of these links with the base-year traffic counts and the forecast and observed ADTs in the forecast year. The table adds an inaccuracy index in the traffic forecasts that was estimated as follows: = − Table III-H-11. Comparison of base-year and mid-year traffic counts and mid-year traffic forecast (CA/T project). Site ID Site Segment Base Year Mid- Year Count Mid-Year Forecast Percent Difference from Forecast1987 2010 2010 1 (M) I-93 Northbound - I-90 On-Ramp to Government Center Off-Ramp 69,000 99,000 100,300 -1% 2 (M) I-93 Northbound - Frontage On-Ramp to I-90 On-Ramp 72,000 77,500 84,600 -8% 3 (M) I-93 Northbound - I-90 Off-Ramp to Mass. Avenue On-Ramp 64,000 52,000 54,000 -4% 4 (M) I-93 Northbound - Southampton to Mass. Avenue 90,000 103,000 113,900 -10% 5 (O) I-93 Northbound - North of Columbia Road 93,000 111,500 124,700 -11% 6 (M) I-93 Southbound - Dewey Square Off-Ramp to barrel split 91,000 91,500 86,300 6% 7 (M) I-93 Southbound - barrel converge to I-90 On-Ramp 89,000 74,500 82,300 -9% 8 (M) I-93 Southbound - Albany On-Ramp to Mass. Avenue Off-Ramp 83,000 115,000 119,300 -4% 9 (O) I-93 Southbound - Southampton to project limit 96,000 114,000 121,600 -6% 10 (O) I-93 Southbound - South of Columbia Road 90,000 108,000 111,300 -3% 11 (N) I-90 Westbound - Ted Williams Tunnel N/A 40,500 47,300 -14% 12 (N) I-90 Eastbound - Ted Williams Tunnel N/A 42,000 51,200 -18% New Links (average traffic) 41,250 49,250 -16% Modified Links (average traffic) 87,500 91,529 -4% Other Links (average traffic) 111,167 119,200 -7% Note: Site 12 in Figure III-H-8 reflects both Sites 11 and 12 (M) Modified; (N) New; (O) Other Source: CTPS Backcasting Report (2014) As Table III-H-11 shows, the traffic forecasts were generally accurate, with forecasting error ranging from -11 to +6% except for the two segments at the Ted Williams Tunnel (#11 and 12). The Ted Williams Tunnel was the only tolled roadway segment in the CA/T project. It also was a completely new segment (unlike the other segments), which may explain its higher percent difference. 3.4 Potential Sources of Forecast Error This section identifies the exogenous forecasts and project assumptions used in the development of the traffic forecasts. Exogenous forecasts are made outside of the immediate traffic forecasting process. Project assumptions are established during project development and serve as the basis for the traffic forecast. Exogenous forecasts and project assumptions are leading sources of forecast error. Population and employment forecasts are examples of exogenous forecasts, and they are commonly identified as a major source of traffic forecasting error. These forecasts are usually made by outside planning agencies, and they are made on a regular basis; that is, they are not prepared for any individual project. During (III-H-5)

Appendix H: Deep Dives III-H-33 project development, these forecasts are revised to match specific assumptions documented by the project team. Past forecasting research has identified several exogenous forecasts and project assumptions as common sources of forecast error, including: Macroeconomic conditions (of the region or study area), Population and employment forecasts, Significant changes in land use, Auto fuel prices, Tolling pricing, sensitivity and price levels, Auto ownership, Changes in technology, Travel times within the study area, and Duration between year forecast produced and opening year. For the CA/T project, Table III-H-12 lists all the exogenous forecasts and project assumptions for which observed data was available. It also includes an assessment of the accuracy of each item. Table III-H-12. List of exogenous forecasts and project assumptions(CA/T project). Items Quantifiable Observed Year 2010 Values Estimated Year 2010 Values Percent Difference Employment * Yes 424,000 538,000 -21% Population * Yes 210,000 198,000 6% Auto Fuel Price (price per gallon) ** Yes $2.86 $2.31 24% Macroeconomic Conditions No Data Source for Observed Value: * CTPS report ** BLS, Office of Energy Efficiency & Renewable Energy, & EPA Only a few exogenous forecasts and project assumptions were evaluated as potential sources of forecast errors for the CA/T project due to the absence of available data. For the CA/T project area, the CA/T project team performed their own population and employment forecasts. For the rest of the Boston Region MPO, the CA/T team adopted MPO’s socio-demographic forecasts that used the trends of fertility, mortality, and other standard demographic metrics. Table III-H-12 shows that the employment forecasts were overestimated, the population forecasts were generally accurate, and the auto fuel prices were underestimated. Information on other typical exogenous forecasts (e.g., macroeconomic conditions, car ownership, travel time, and value of time) was unavailable. For fuel price, a proxy fuel price forecast was estimated by multiplying the 1991 average gasoline price with an annual inflation rate between 1991 and 2010. 3.5 Sources Contributing to Forecast Error Building upon the items discussed in Section 3.4, this section attempts to identify items that are important sources of forecast error or percent difference from forecast and, for those so identified, attempts to quantify how much it would change the forecast if the forecasters had accurate information about the item.

III-H-34 Traffic Forecasting Accuracy Assessment Research Adjusted forecasts for the critical roadways were computed by applying an elasticity to the relative change between the actual and predicted values for each item in Section 3.4. Only those items that could be quantified and deemed important for this project were adjusted. The effect on the forecast can be quantified in this way: First, the following equation is used to calculate the change in forecast value, a delta between the opening-year forecast and the actual observed traffic count in the opening year. Change in Forecast Value = ( − ) ( ⁄ Second, a factor of the effect on the forecast is calculated by exponentiating an elasticity of the common source errors, and a natural-log of the change rate in the forecast value is calculated. This factor is applied to the actual forecast volume to generate an adjusted forecast. Effect on Forecast = ( ∗ (1+ ℎ )) − 1 Adjusted Forecast = (1 + ) ∗ This deep dive analysis adopted the best elasticity values possible as identified by Ewing et al. (2014) via their cross-sectional and longitudinal models together and in other transportation literature (Dong et al. 2012; Dunkerley, Rohr, and Daly 2014). It is important to note that the elasticity values identified by Ewing et al. (2014) relate to VMT, not traffic volumes. We were not able to find elasticity values specifically for traffic volumes with respect to employment, population, and fuel price. In addition, we were not able to find the elasticity value of VMT, nor traffic volume with respect to employment. To this end, this elasticity study in NCHRP 08-110 reflects two assumptions: (1) the elasticity values of VMT with respect to population and fuel price are close to the elasticity values of traffic volumes given a high correlation between VMT and traffic volumes, and (2) the elasticity values regarding employment are close to the ones for per capita income, again because of their high correlation. The elasticity values used in the CA/T study were: 0.75 for population, 0.3 for capita income (employment), and 0.2 for fuel price. The results of quantifying the effect on the forecast are shown in Table III-H-13. The original forecast value was successively adjusted for each of the items that were identified as contributing sources of forecasting error for all the segments. The final remaining percentage differences after all adjustments are shown in Table III-H-13. Table III-H-13 is sorted by the largest-to-smallest of the “remaining percent difference from forecast.” The segment IDs reflect the sites shown in Figure III- H-8. (III-H-2) (III-H-3) (III-H-4) )

Table III-H-13. Forecast adjustment table based on elasticities (CA/T project). Seg# Items Actual Value Forecast Value Change in Forecast Value Elasticity Effect on Forecast Actual Forecast Volume Adj. Forecast Volume Remaining Percent Difference Given Adj. Forecast 1 Employment 424,000 538,000 -21% 0.30 -7% 100,300 93,385 6.01% 1 Population 210,000 198,000 6% 0.75 5% 93,385 97,598 1.44% 1 Fuel Price 2.86 2.31 24% (0.20) -4% 97,598 93,504 5.88% 1 Original Traffic Forecast 99,000 100,300 1% N/A N/A 1 Adjusted Traffic Forecast N/A N/A N/A 100,300 93,504 5.88% 2 Employment 424,000 538,000 -21% 0.30 -7% 84,600 78,767 -1.61% 2 Population 210,000 198,000 6% 0.75 5% 78,767 82,321 -5.86% 2 Fuel Price 2.86 2.31 24% (0.20) -4% 82,321 78,868 -1.73% 2 Original Traffic Forecast 77,500 84,600 9% N/A N/A 2 Adjusted Traffic Forecast N/A N/A N/A 84,600 78,868 -1.73% 3 Employment 424,000 538,000 -21% 0.30 -7% 54,000 50,277 3.43% 3 Population 210,000 198,000 6% 0.75 5% 50,277 52,545 -1.04% 3 Fuel Price 2.86 2.31 24% (0.20) -4% 52,545 50,341 3.30% 3 Original Traffic Forecast 52,000 54,000 4% N/A N/A 3 Adjusted Traffic Forecast N/A N/A N/A 54,000 50,341 3.30% 4 Employment 424,000 538,000 -21% 0.30 -7% 113,900 106,047 -2.87% 4 Population 210,000 198,000 6% 0.75 5% 106,047 110,832 -7.07% 4 Fuel Price 2.86 2.31 24% (0.20) -4% 110,832 106,182 -3.00% 4 Original Traffic Forecast 103,000 113,900 11% N/A N/A 4 Adjusted Traffic Forecast N/A N/A N/A 113,900 106,182 -3.00% 5 Employment 424,000 538,000 -21% 0.30 -7% 124,700 116,102 -3.96% 5 Population 210,000 198,000 6% 0.75 5% 116,102 121,341 -8.11% 5 Fuel Price 2.86 2.31 24% (0.20) -4% 121,341 116,251 -4.09% 5 Original Traffic Forecast 111,500 124,700 12% N/A N/A 5 Adjusted Traffic Forecast N/A N/A N/A 124,700 116,251 -4.09% 6 Employment 424,000 538,000 -21% 0.30 -7% 86,300 80,350 13.88%

Seg# Items Actual Value Forecast Value Change in Forecast Value Elasticity Effect on Forecast Actual Forecast Volume Adj. Forecast Volume Remaining Percent Difference Given Adj. Forecast 6 Population 210,000 198,000 6% 0.75 5% 80,350 83,975 8.96% 6 Fuel Price 2.86 2.31 24% (0.20) -4% 83,975 80,452 13.73% 6 Original Traffic Forecast 91,500 86,300 -6% N/A N/A 6 Adjusted Traffic Forecast N/A N/A N/A 86,300 80,452 13.73% 7 Employment 424,000 538,000 -21% 0.30 -7% 82,300 76,626 -2.77% 7 Population 210,000 198,000 6% 0.75 5% 76,626 80,083 -6.97% 7 Fuel Price 2.86 2.31 24% (0.20) -4% 80,083 76,724 -2.90% 7 Original Traffic Forecast 74,500 82,300 10% N/A N/A 7 Adjusted Traffic Forecast N/A N/A N/A 82,300 76,724 -2.90% 8 Employment 424,000 538,000 -21% 0.30 -7% 119,300 111,075 3.53% 8 Population 210,000 198,000 6% 0.75 5% 111,075 116,086 -0.94% 8 Fuel Price 2.86 2.31 24% (0.20) -4% 116,086 111,216 3.40% 8 Original Traffic Forecast 115,000 119,300 4% N/A N/A 8 Adjusted Traffic Forecast N/A N/A N/A 119,300 111,216 3.40% 9 Employment 424,000 538,000 -21% 0.30 -7% 121,600 113,216 0.69% 9 Population 210,000 198,000 6% 0.75 5% 113,216 118,324 -3.65% 9 Fuel Price 2.86 2.31 24% (0.20) -4% 118,324 113,361 0.56% 9 Original Traffic Forecast 114,000 121,600 7% N/A N/A 9 Adjusted Traffic Forecast N/A N/A N/A 121,600 113,361 0.56% 10 Employment 424,000 538,000 -21% 0.30 -7% 111,300 103,626 4.22% 10 Population 210,000 198,000 6% 0.75 5% 103,626 108,302 -0.28% 10 Fuel Price 2.86 2.31 24% (0.20) -4% 108,302 103,759 4.09% 10 Original Traffic Forecast 108,000 111,300 3% N/A N/A 10 Adjusted Traffic Forecast N/A N/A N/A 111,300 103,759 4.09% 11 Employment 424,000 538,000 -21% 0.30 -7% 47,300 44,039 -8.04% 11 Population 210,000 198,000 6% 0.75 5% 44,039 46,026 -12.01% 11 Fuel Price 2.86 2.31 24% (0.20) -4% 46,026 44,095 -8.15% (continued on next page)

Table III-H-13 (Continued). Seg# Items Actual Value Forecast Value Change in Forecast Value Elasticity Effect on Forecast Actual Forecast Volume Adj. Forecast Volume Remaining Percent Difference Given Adj. Forecast 11 Original Traffic Forecast 40,500 47,300 17% N/A N/A 11 Adjusted Traffic Forecast N/A N/A N/A 47,300 44,095 -8.15% 12 Employment 424,000 538,000 -21% 0.30 -7% 51,200 47,670 -11.89% 12 Population 210,000 198,000 6% 0.75 5% 47,670 49,821 -15.70% 12 Fuel Price 2.86 2.31 24% (0.20) -4% 49,821 47,731 -12.01% 12 Original Traffic Forecast 42,000 51,200 22% N/A N/A 12 Adjusted Traffic Forecast N/A N/A N/A 51,200 47,731 -12.01% New Extension Employment 424,000 538,000 -21% 0.30 -7% 98,500 91,709 -10.04% Population 210,000 198,000 6% 0.75 5% 91,709 95,847 -13.93% Fuel Price 2.86 2.31 24% (0.20) -4% 95,847 91,826 -10.16% Original Traffic Forecast 82,500 98,500 19% N/A N/A Adjusted Traffic Forecast N/A N/A N/A 98,500 91,826 -10.16% Modified Links Employment 424,000 538,000 -21% 0.30 -7% 640,700 596,527 2.68% Population 210,000 198,000 6% 0.75 5% 596,527 623,441 -1.75% Fuel Price 2.86 2.31 24% (0.20) -4% 623,441 597,287 2.55% Original Traffic Forecast 612,500 640,700 5% N/A N/A Adjusted Traffic Forecast N/A N/A N/A 640,700 597,287 2.55% Other Links Employment 424,000 538,000 -21% 0.30 -7% 357,600 332,945 0.17% Population 210,000 198,000 6% 0.75 5% 332,945 347,967 -4.16% Fuel Price 2.86 2.31 24% (0.20) -4% 347,967 333,370 0.04% Original Traffic Forecast 333,500 357,600 7% N/A N/A Adjusted Traffic Forecast N/A N/A N/A 357,600 333,370 0.04%

III-H-38 Traffic Forecasting Accuracy Assessment Research In general, the adjustments resulted in improved traffic forecast accuracy. Nine of the 12 study roadways experienced a decrease in the forecast percent difference from forecast; that is, the accuracy of the traffic forecasts would have been better if the exogenous factors had been accurately forecast. 3.6 Discussion The CA/T project replaced the deteriorating elevated I-93 central artery with a pair of 1.5-mile underground expressway tunnels, built the new 1.6-mile Ted Williams Tunnel to Logan Airport, extended the I-90 to the Ted Williams Tunnel, and built two new bridges over the Charles River, six interchanges, and the Rose Kennedy Greenway in the space vacated by the previous elevated I-93 central artery in Boston, Massachusetts. The CTPS’s backcasting report showed that roadways in the CA/T project were generally overestimated, ranging from 1% to 22%, with one roadway segment underestimated by 6%. Overall, traffic forecasting accuracy improved after correcting the exogenous forecasts and project assumptions. Nine of 12 roadway segments experienced a reduced percent difference from forecast as a result. It should be noted that although abundant documentation exists on the CA/T projects, virtually all of it is associated with project management, construction, project finance, and economic impacts. It is unknown whether risk and uncertainty were considered during the project due to the absence of documentation on the subject. For future forecasting efforts, it is suggested that a copy of the forecasting documentation and assumptions be archived along with the travel model files used to generate the forecasts. 4 Cynthiana Bypass, Cynthiana, Kentucky 4.1 Introduction The Cynthiana Bypass is a 2-lane state highway bypass project located in Cynthiana, Kentucky. This report, written in June 2018, assesses the reliability and accuracy of traffic forecasts for the Cynthiana Bypass. Traffic forecasts for the project were prepared in 1994 for a 2010 opening year. (Traffic forecasts also were provided in 2003 for 2025 using growth rates and diversion assumptions). The project opened in about 2012. Post-opening traffic counts were available for the years 2014 and later. Section 4.2 describes the project. Section 4.3 compares the predicted and actual traffic volumes for all roadways in the study area where post-opening traffic counts were available. Section 4.4 enumerates the exogenous forecasts and sources of forecast error for the project. It also includes an assessment of the accuracy of the exogenous forecasts. Section 4.5 attempts to identify items discussed in Section 4.4 that were important sources of forecast error and, for those so identified, attempts to quantify how much it would change the forecast if the forecasters had accurate information about each item. Section 4.6 summarizes the findings.

Appendix H: Deep Dives III-H-39 4.2 Project Description The study area included the Cynthiana city limits and immediate environs in Harrison County, Kentucky. The project created a bypass to the west of the city, starting at a southern terminus where US-62S and US-27S meet, and extending northwards to a point north of the city along Main Street/US-27N. The length of the bypass is 3.6 miles, and includes a new bridge across the south fork of the Licking River, north of the city. Source: Map data: Google Maps, annotated by NCHRP 08-110 project team Figure III-H-9. Project corridor (Cynthiana Bypass).

III-H-40 Traffic Forecasting Accuracy Assessment Research 4.3 Predicted–Actual Comparison of Traffic Forecasts The Kentucky Transportation Cabinet (KYTC) and their consultants provided travel demand model files and some documentation for this effort. These model runs and scattered memos/documentation were used to analyze and report the predicted traffic on the project. An urban area transportation study was first conducted for Cynthiana in 1970. The study area included an area roughly 3.5 miles in diameter centered on downtown, which included both the incorporated area of Cynthiana and some adjacent area. During the late 1980s, the 1970 study and plans were updated in view of changing development in the area. A 1989 Cynthiana Urban Area Transportation Study Technical Document describes the development of a travel demand model with the base year 1988 and future year 2010, but this document does not address the proposed bypass. A loaded TransCAD network, derived from an original MINUTP model, along with various MINUTP data input files for a 1994 base year and 2020 future year (including the bypass) was, however, made available to the project team. Therefore, documentation of the project forecasts was unavailable at the time of writing. Aspects related to project costs, exact opening year, the importance of the project to the local community, and other characteristics could not be determined. Using historical Google Earth images, the opening year was estimated to have occurred between 2010, when construction had not yet begun, and 2014, when the project was fully constructed and open to the public. Figure III-H-9 shows the project corridor and the segments chosen for model assessment. (The deep dive and Figure III-H-9 omit Segment E because this segment was never built.) Traffic growth factors for interim years were made available and used to estimate opening-year (2014) model traffic (backed down from the 2020 forecast). Socioeconomic data for the opening year were available in the 1989 document for a 1988 base year and 2010 forecast year, and the 2010 forecast socioeconomic data could be compared to actual census data from 2010 for the pre-bypass case. Several years were of interest in evaluating the performance of the Cynthiana Bypass travel model(s) (see Table III-H-14): 1988—The base year of the original model, for which calibration/validation documentation was available, 1988 was used to forecast 2010 traffic without the bypass, actual counts, and documented actual and forecast socioeconomic data available; 1994—The base year of the updated model, used to forecast 2020 traffic with the bypass, for which actual counts were available, loaded model files were available, and limited SE data were available in model files, but no documentation was available; 2010—A Census year, and the original forecast year without the bypass, for which actual counts were available; 2014—The first full year with the bypass constructed; also the first year with actual counts available for the network including the bypass; and 2020—The forecast year for the updated model, for which estimates could be growth-factored back to 2014 for comparison.

Appendix H: Deep Dives III-H-41 Table III-H-14. Availability of data for Cynthiana Bypass project. Year Base Model Model Format Calibration/ Validation Docs? Forecast Model Traffic Counts (Network) Traffic Counts (Bypass) Pop/SE Data (actual) Pop/SE Data (Forecast) Pop/SE Docs? 1988 YES MINUTP YES 2010 YES - YES YES (2010) YES 1994 YES MINUTP NO 2020 YES - NO NO NO 2010 (1988 base) 1988 MINUTP - YES (no bypass) YES - YES - - 2014 - - - YES* YES YES NO NO - 2020 (1994 base) 1994 MINUTP and TRANSCA D (loaded networks) - YES (w/ bypass) - - - - - * Estimated by backing down 2020 model forecasts Accuracy of Employment Forecast Employment for the study area was estimated to be 4410 in 1988. An inspection of the aerial photography around Cynthiana suggested that 95% or more of the area’s employment was distributed within the city limits. Using this 95% figure, the 1988 city-limit employment was estimated to be about 4190. The actual 2010 Census employment figure within the city limits was 3905. The actual average annual growth rate in employment for the 22-year period was -0.32 percent, but the original modelers had assumed employment in the study area would grow to 4850 by 2010—a growth rate of +0.43 percent. Reflecting this assumption, the model overpredicted employment by some 18 percent. Accuracy of Population Forecast The 1980 Census indicated a population for Cynthiana proper of 5,881 persons. In 1988, the population of Cynthiana proper was 6,016, and the study area population was estimated to be 7,685. Therefore, approximately 78.3% of the study area population was inside the city limits in 1988. When the future-year 2010 model was built, the population for the Cynthiana study area was projected to grow to 8,455, a 10% increase over 1988 levels. The actual 2010 Census population for Cynthiana proper was 6,402, a 6.4% increase over 1988 levels. The population was therefore overestimated by approximately 3%. Accuracy of External Traffic Forecast The original model documentation indicated that a growth factor of 2.5% per year was to be used for future external traffic. A reconstructed TransCAD model was run for the “opening” year forecast for 2020. Traffic volumes from the 2020 models (original and re-created) were reduced by 2.5% per year for 6 years, and compared to 2014 (or 2014 estimated) ground counts. Table III-H-15

III-H-42 Traffic Forecasting Accuracy Assessment Research shows the comparison of external–external (EE) and external–internal (EI) trips at each cordon station between the original 2014 forecasts and the 2014 counts. In general, the model overestimated external trips by 76%. Table III-H-15. External trips: forecasts and percent differences. Highway EE Trips 1988 EE Trips 2014 (2.5% Growth) EI Trips Produced 2014 2014 Tot. Vol. 2014 Count Diff. Percent Diff. 356 W 185 352 899 1597 612 -985 -62% 36 N 440 836 2816 4490 3173 -1317 -29% 27 N 902 1714 3235 6663 3927 -2736 -41% 62 N 372 707 2096 3510 3476 -34 -1% 392 N 147 279 1198 1759 1250 -509 -29% 32 E 253 481 1897 2856 3188 332 12% 982 S 77 146 799 1089 1925 836 77% 27 S 850 1615 2816 6038 4740 -1298 -21% 62 S 714 1357 3095 5802 7449 1647 28% 32 W 158 300 1118 1718 1666 -52 -3% Accuracy of External Estimates The original model forecast a total of 9,031 EE trips in 2010. By applying the same percentage of EE trips at each cordon station used in the original model to the 2014 ground counts, an updated estimate was created that forecast 5,123 EE trips. The original model thus overestimated EE trips by some 76% ((9,031–5,123)/5,123). Figure III-H-10 (next page) shows the Cynthiana Bypass study area link volumes.

Appendix H: Deep Dives III-H-43 Source: Map created by NCHRP 08-110 project team Figure III-H-10. Cynthiana Bypass study area link volumes. Model Runs Model runs included loaded highway networks for both the base and opening years. As the original models were written in MINUTP and that program was not available to the team, original model data was used to recreate the model in TransCAD. A 2020 TransCAD version of the model was provided to the research team by the KYTC (they had already converted it); however, only a loaded network was available (no TAZ map or trip-generation data were included in the TransCAD version provided). Therefore, the model team took original data from the MINUTP text files and limited model documentation to recreate base-year (1988) and opening-year (2010) variants of the model.

III-H-44 Traffic Forecasting Accuracy Assessment Research For the opening-year forecasts to be consistent with the additional model runs made to quantify sources of forecasting error as described in Section 4.5, the opening-year scenario run was remade using TransCAD. As no base year loaded network files or data was available, a new 2020 model was created using the original forecast data for 2010, which was growth factored up to 2020 using the original assumptions of 2.5% growth. The loaded network generated from this new model run was used to report the link level opening-year forecasts. Also, given that the Cynthiana Bypass was not opened until about 2012, all model forecasts and ground counts were adjusted to 2014 for purposes of comparison. It should be noted that very little (around 3%) difference was seen in the model volumes between the new 2020 model run and the original model run provided by KYTC. A total of four links with available ADT traffic counts were identified in the project corridor. Table III-H-16 lists each of these links with their forecast and observed ADT volumes. The table includes an inaccuracy index in traffic forecasts that was estimated as: = − The first four segments constituted the Cynthiana Bypass project (see Figure III-H-9). Table III-H-16. Traffic volume accuracy assessment (Cynthiana Bypass project). Seg# Project Segment and Direction Opening-Year Count (2014) Opening-Year Forecast (Factored to 2014) Percent Difference from Forecast A US-62 to KY-32, 2 Lanes, Item no. 6-119.02, Source: V 2851 4372 -34.79% B KY-32 to KY-356, 2 Lanes, Item No. 6-119.02 3630 5152 -29.54% C KY-356 to KY-36, 2 Lanes, Item no 6-119.02 3039 4466 -31.95% D KY-36 to US-27 2975 3091 -3.75% 4.4 Potential Sources of Forecast Error This section identifies the exogenous forecasts and project assumptions used in the development of the traffic forecasts. Exogenous forecasts are made outside of the immediate traffic forecasting process. Project assumptions are established during project development and serve as the basis for the traffic forecast. Exogenous forecasts and project assumptions are leading sources of forecast error. Examples are population and employment forecasts, which are commonly identified as major sources of traffic forecasting error. These forecasts are usually made by outside planning agencies, and they are made on a regular basis; that is, they are not prepared for any individual project. During project development, these forecasts are revised to match the specific assumptions documented by the project team. In this example, population and employment forecasts are both exogenous forecasts and involve project assumptions. (III-H-1)

Appendix H: Deep Dives III-H-45 Table III-H-17 lists all exogenous forecasts and project assumptions for which observed data were available for the Cynthiana Bypass project. The table also includes an assessment of the accuracy of each item. Table III-H-17. Input accuracy assessment table (Cynthiana Bypass project). Items Quantifiable Observed Opening-Year Values * Estimated Opening-Year Values ** Percent Difference Employment * Yes 4,111 4,850 -15% Population ** Yes 8,179 8,455 -3% External Traffic Yes 5,123 9,031 -43% Data Sources: * American FactFinder plus assumptions of city/area split (78.3% pop., 95% empl.) ** Cynthiana UATS Technical Document The model documentation, in particular the Cynthiana Urban Area Transportation Study Technical Document, forecast the traffic for the year 2010. The population and employment statistics also were estimated for the year 2010. The project was not concluded and did not open to traffic before June 12, 2013,3 but for the NCHRP 08-110 assessment the demographic information was taken for 2010 in order to be consistent with the model assumptions. Reviewing the model specification, one of the key absences noticed is the assignment of friction factors. According to the Cynthiana UATS Technical Document, “because an internal origin- destination survey was not made in Cynthiana, definite trip table frequency information for base-year internal trips was not available. Therefore travel time factors (friction factors) could not be calculated. For the Cynthiana study, the factor for each trip length interval was initially given a value of one (1). This means that the trip length of travel time does not affect the trip making decision.” In the future year (2010) model development, the travel demand was considered directly related to the same factors that influence existing travel demand (i.e., population and employment). 4.5 Sources Contributing to Forecast Error Building upon the items discussed in Section 4.4, this section attempts to identify items that were important sources of forecast error and, for the items so identified, attempts to quantify how much it would change the forecast if the forecasters had accurate information about the item. Adjusted forecasts for the critical roadways were computed by applying an elasticity to the relative change between the actual and predicted values for each item identified in Section 4.4. The effect on the forecast can be quantified in this way: First, the change in forecast value is calculated, a delta between the opening-year forecast and the actual observed traffic count in the opening year. Change in Forecast Value = ( − ) ( )⁄ 3 https://www.cynthianademocrat.com/content/cynthiana-bypass-round-about-open-wednesday (III-H-2)

III-H-46 Traffic Forecasting Accuracy Assessment Research Second, a factor of the effect on the forecast is calculated by exponentiating an elasticity of the common source errors, and a natural-log of the change rate in forecast value is calculated. This factor is then applied to the actual forecast volume to generate an adjusted forecast. Effect on Forecast = ( ∗ (1+ ℎ )) − 1 Adjusted Forecast = (1 + ) ∗ This deep dive analysis adopted the best elasticity values possible based on those identified by Ewing et al. (2014) via their cross-sectional and longitudinal models together and from other transportation literature (Dong et al. 2012; Dunkerley, Rohr, and Daly 2014). It is important to note that the elasticity values identified by Ewing et al. (2014) relate to VMT, not traffic volumes. To the best of our knowledge and the literature review, there is no literature investigating elasticity values for traffic volumes with respect to employment, population, and fuel price. Also, none of the literature reviewed discussed the elasticity value of VMT, nor traffic volume, with respect to employment. To this end, the elasticity study in NCHRP 08-110 reflects two assumptions: (1) the elasticity values of VMT with respect to population are close to the elasticity values of traffic volumes, given a high correlation between VMT and traffic volumes, and (2) the elasticity values regarding employment are close to the ones for per capita income, again because of their high correlation. The results of quantifying the effect on the forecast are shown in Table III-H-18. Table III-H-18. Forecast adjustment table based on elasticities for all segments (Cynthiana Bypass project). Seg# Items Actual Value Forecast Value Change in Forecast Value Elasticity Effect on Forecast Actual Forecast Volume Adj. Forecast Volume Remaining Percent Difference Given Adj. Forecast A Employment 4,850 4,111 -15% 1.20 -18% 4,372 3,585 15% A Population/Household 8,455 8,179 -3% (0.10) 0% 3,585 3,597 127% A Original Traffic Forecast 4372 2857 -35% N/A N/A A Adjusted Traffic Forecast N/A N/A N/A 4,372 3,597 -21% B Employment 4,850 4,111 -15% (1.40) 26% 5,152 6,494 -37% B Population/Household 8,455 8,179 -3% (2.70) 9% 6,494 7,102 15% B Original Traffic Forecast 5152 3630 -30% N/A N/A B Adjusted Traffic Forecast N/A N/A N/A 5,152 7,102 -49% C Employment 4,850 4,111 -15% (4.00) 94% 4,466 8,652 -52% C Population/Household 8,455 8,179 -3% (5.30) 19% 8,652 10,315 -21% C Original Traffic Forecast 4466 3039 -32% N/A N/A C Adjusted Traffic Forecast N/A N/A N/A 4,466 10,315 -71% D Employment 4,850 4,111 -15% (6.60) 198% 3,091 9,203 -55% D Population/Household 8,455 8,179 -3% (7.90) 30% 9,203 11,962 -32% D Original Traffic Forecast 3091 2975 -4% N/A N/A D Adjusted Traffic Forecast N/A N/A N/A 3,091 11,962 -75% The original forecast value was successively adjusted for each of the items identified as contributing sources of forecasting error, and the final remaining percentage differences after all (III-H-4) (III-H-3)

Appendix H: Deep Dives III-H-47 adjustments are shown in the table. Table III-H-18 shows the detailed elasticity-based adjustments made for all the segments. The most significant impact on traffic volumes was due to overestimated external traffic. Over-optimistic employment projections also contributed to model error. Using elasticity-based adjustments, the forecast percentage differences were improved on all the segments of the bypass, though not significantly so. In addition to the elasticity-based adjustments, the travel model used to produce the traffic forecasts was rerun using corrected exogenous forecasts and project assumptions. The same items identified in Section 4.4 were adjusted sequentially to obtain new model volumes. The results of this process for all segments are shown in Table III-H-19. Table III-H-19. Forecast adjustments by model (Cynthiana Bypass project). Seg# Items Old Model Volume Elasticity Adjusted Value New Model Volume Observed Volume Difference (Observed - New) Elasticity Percent Diff. from Observed Model Percent Diff. from Observed Volume Model Percent Diff. from Old Model A Employment Adjustments Only 4372 3585 4147 2851 -1296 -20.47% -31.25% 5.4% B 5152 6494 4965 3630 -1335 -44.10% -26.89% 3.8% C 4466 8652 4440 3039 -1401 -64.88% -31.55% 0.6% D 3091 9203 3091 2975 -116 -67.67% -3.75% 0.0% Project Total 4270.25 6983.5 4160.75 3123.75 -1037 -55.27% -24.92% 2.6% A Population and Employment Adjustments 4372 3597 4140 2851 -1289 -20.74% -31.14% 5.6% B 5152 7102 4951 3630 -1321 -48.89% -26.68% 4.1% C 4466 10315 4429 3039 -1390 -70.54% -31.38% 0.8% D 3091 11962 3087 2975 -112 -75.13% -3.63% 0.1% Project Total 4270.25 8244 4151.75 3123.75 -1028 -62.11% -24.76% 2.9% A External Adjustments Only 4372 Na 2963 2851 -112 -3.78% 47.6% B 5152 Na 3714 3630 -84 -2.26% 38.7% C 4466 Na 3090 3039 -51 -1.65% 44.5% D 3091 Na 1952 2975 1023 52.41% 58.4% Project Total 4270.25 2929.75 3123.75 194 6.62% 45.8% A All Adjustments 4372 Na 2823 2851 28 0.99% 54.9% B 5152 Na 3640 3630 -10 -0.27% 41.5% C 4466 Na 3150 3039 -111 -3.52% 41.8% D 3091 Na 1988 2975 987 49.65% 55.5% Project Total 4270.25 2900.25 3123.75 223.5 7.71% 47.2%

III-H-48 Traffic Forecasting Accuracy Assessment Research Correcting for Employment Adjusting for the employment overestimate, the model was re-rerun with an employment correction factor of 0.8201 (1-(4608-3905)/3905). With this correction, the model root mean square error (RMSE) decreased from 42 to 40, and from 41 to 37 on the bypass segments. Improving the employment forecast provided a modest improvement to the model. Correcting for Population Adjusting for the population overestimate, the model was re-rerun with a correction factor of 0.9662 (1-(6618-6402)/6402). Making this correction actually increased the model RMSE from 42 to 44, however, and the RMSE on the bypass segments would have increased from 41 to 44 had the population forecast been accurate. Obviously, population forecasting was not the problem. Correcting for Population and Employment Improving both population and employment data had the same effect of improving the employment forecast alone: the model RMSE decreased from 42 to 40, and the RMSE on the bypass segments decreased from 41 to 37. Overall, the employment and employment/population adjusted forecasts using the model were very similar to those obtained from the elasticity-based adjustments, especially on the bypass. Correcting for External Trips The actual external-external (EE) trips are 43% lower than forecast ((5123-9031)/9031), so a correction factor of 0.5672 (5123/9031) was applied to the original EE matrix to rerun the model. Furthermore, the original model held internal attractions constant in the production-attraction balancing of external-internal (EI)trips. As we usually have more confidence in cordon counts than in the sum of internal EI attractions, productions at the external stations were held constant in the revised model. The model was rerun with improved external forecasts. This improvement alone decreased the overall model RMSE from 42 to 39, but—impressively—it decreased the RMSE of the bypass link RMSE from 41 to 17. Correcting for Population, Employment, and External Trips Lastly, the model was rerun with improved population, employment, and external forecasts. Together, these improvements resulted in decreases similar to the improvements observed by improving the external estimates alone. Specifically, the overall model RMSE decreased from 42 to 37, and the bypass links RMSE again decreased from 41 to 17. 4.6 Discussion The traffic forecasts on the Cynthiana Bypass were generally overestimated by about 45%, with the notable exception of the northernmost section, which was estimated to within 4% of observed values.

Appendix H: Deep Dives III-H-49 As would be expected for a bypass project, the biggest source of error in the model forecast was the overestimated growth factor (2.5% per year) in external trips. Three out of four segments of the project showed a significant improvement after accounting for the corrected external trips. The project opened in 2012, which was shortly after the peak of the economic recession and during a time of high gas prices. As a result, overestimation of employment in the opening year was a contributor to the forecasting errors in this project. Population forecasts were very similar to the observed values and did not contribute to the forecasting error (in fact, correcting for actual population alone made the forecasts a bit worse). Risk and uncertainty were not explicitly considered in the traffic forecasts. Project documentation was not archived by the project owners. Fortunately, a copy of the documentation was obtained from the consultant who happened to keep a paper copy in her personal files (she had long since left employment at the consulting company that was contracted to do the study). For future forecasting efforts, it is suggested that copies of the project and traffic forecasting documentation be saved along with the actual models used to generate the forecasts by the project owners (in this case, the state highway authority). 5 South Bay Expressway, San Diego, California 5.1 Introduction The South Bay Expressway (SBX) is a 9.2-mile tolled highway segment of SR-125 in eastern San Diego, California. SBX generally runs north-south from SR-54 near the Sweetwater Reservoir to SR-905/SR-11 in Otay Mesa, California, near the U.S.–Mexico Border. A 3.2-mile untolled link to the existing freeway network at the northern end was publicly funded and built with the construction of the private toll road. Originally developed as a public-private partnership (P3), SBX opened in November 2007. Initial traffic and revenue were below expectations and the company was involved in ongoing litigation with contractors. In March 2010, the operator filed for bankruptcy. In July 2011, the San Diego Association of Governments (SANDAG) agreed to purchase the lease from the operator, taking control of the remainder of the 35-year lease in November 2011.4 This report, written in July 2018, assesses the utility, reliability, and accuracy of traffic forecasts for the South Bay Expressway. Traffic forecasts for the project were prepared in 2002 for the 2006, 2010, and 2020 forecast year(s). The project opened in 2007 although observed traffic count data are unavailable for the early years of the project. Where observed traffic data is available, they are in various formats, forms, and locations, prohibiting reasonable comparisons. The general narrative of the early years of the SBX is that traffic and revenue were well below forecasts, which in addition to ongoing litigation with contractors, caused the operator to file for bankruptcy. Section 5.2 of this report describes the project. Section 5.3 describes the traffic forecast method. Section 5.4 enumerates the exogenous forecasts and sources of forecast error for the project. 4 https://www.transportation.gov/tifia/financed-projects/south-bay-expressway

III-H-50 Traffic Forecasting Accuracy Assessment Research 5.2 Project Description The original study area boundary was essentially the entire San Diego region. SBX is the easternmost north-south expressway in San Diego. It was originally developed to accommodate the rapidly growing residential and industrial South Bay area and to provide improved access to the US- Mexico Border Crossing facility at Otay Mesa. The original SBX analysis was for the first toll facility in San Diego. The SBX was developed as permitted by California AB 680, passed by the California legislature in 1989. Under the agreement, the concessionaire developed the project and constructed the road in return for operating and maintaining the facility and collecting toll revenue for 35 years, until 2042. As per the agreement, the State of California owns the facility, but leases it to the concessionaire. After the original concessionaire declared bankruptcy, SANDAG purchased the concession in December 2011 and will retain tolling control until the facility reverts back to Caltrans in 2042. As opposed to maximizing revenue on the facility, SANDAG sets the toll prices to relieve congestion on the I-5 and I-805. A map of the corridor and current toll rates are shown in Figure III-H-11. Sources: Left: Google Maps; Right: http://www.sr125.com Figure III-H-11. Project study area (South Bay Expressway). 5.3 Traffic Forecasts Method The original traffic and revenue models were not available for detailed investigation and comparison. The comparisons in the NCHRP 08-110 study were therefore based on reviews of the traffic and revenue forecasting report and a technical due diligence analysis of the report. The general process of the forecasting was consistent with established practice, although as discussed in the next section, the model inputs and assumptions used to develop the forecasts were not appropriate. The project development traffic and revenue analysis models were based on the “Series 9” SANDAG Regional Travel Demand Forecasting Model, which also was not available. Like most corridor planning and forecasting analyses, the traffic and revenue forecasting process utilized the elements of the model to develop forecasts for future years, in this case for 2005, 2010, 2015, and

Appendix H: Deep Dives III-H-51 2020. Trip tables from the base SANDAG process were modified using information from new surveys and border crossing information. The traffic forecasts were developed with the TRANPLAN equilibrium highway assignment procedure. The highway assignment developed a free path and a tolled path (where appropriate) between all origins and destinations, and calculated the percentage of trips using the tolled path utilizing the value of time for the travelers. This procedure remains an accepted forecasting method for individual toll facilities. The model process followed an accepted method of modeling the a.m. and p.m. peak periods, as well as the interpeak, to develop traffic profiles in congested and uncongested periods. The project’s technical due diligence report concluded that the “analyses are based on generally accept industry standards and use reasonable modeling techniques” (Louis Berger Group). The report cautioned about socioeconomic data growth levels and competing free network improvements. 5.4 Potential Sources of Forecast Error This section identifies the exogenous forecasts and project assumptions used in the development of the traffic forecasts which contributed to the forecast error. Exogenous forecasts are made outside of the immediate traffic forecasting process. Project assumptions are established during project development and serve as the basis for the traffic and revenue forecast. Exogenous forecasts and project assumptions are potential sources of forecast error. In this case there is no ability to test the sensitivity of the input exogenous forecasts or assumptions or even compare to actual traffic and revenue values. However, analysis of several rounds of forecasting reports and limited observed traffic and revenue characteristics and comparison of input assumptions to the observed values leads us to surmise the sources of forecast error. Toll Rates The actual toll rates applied to SBX were higher than projected in the project development stage. Unplanned toll increases in the early years of operation further distanced the SBX full length toll from the project development toll rate. After SANDAG acquired the concession in 2011 and took over the operation of the facilities, they lowered the tolls in 2012, and have kept tolls the same in nominal terms, meaning the toll is lower in real terms year over year. Figure III-H-12 shows the project development and actual and projected toll rates in real (2000$) and nominal terms. SANDAG has no plans to raise or lower the toll, although it makes sense that SANDAG will adjust the toll rate for inflation in the future (although this is not reflected in Figure III-H-12, the projected toll assumes the $2.75 continues indefinitely). Note that future conversion between real and nominal tolls is based on an assumed 2.0% per year CPI growth rate into the future. The project development input toll estimates assumed ETC (Fastrack) tolls would increase with inflation. Real tolls (in 2000$) were assumed to be $2.25 for all offpeak trips, $2.50 from 2007 through 2014, increasing to $2.75 in 2015. The actual ETC tolls applied on SBX were $3.50 in 2007, raising to $3.85 in 2011. The tolls were reduced by SANDAG to current levels of $2.75 in 2012. The tolls remain unchanged (not adjusted for inflation) at $2.75 for Fastrak and $3.50 for cash. Current tolls are 1/3 lower than the tolls assumed in the project development forecasts. This analysis focused on the full-length ETC toll. Cash tolls were designed that cash tolls were as high or higher than the maximum ETC toll rate at each gate. This made some cash toll rates much higher than the ETC rate.

III-H-52 Traffic Forecasting Accuracy Assessment Research Figure III-H-12. Model and actual full-length ETC tolls on SBX. Socioeconomic Growth Direct comparison of the socioeconomic variables used in the forecasting is impossible due to the lack of model files and the data presented in the model. The projected socioeconomic data used to develop the traffic and revenue forecasts were households and acres of non-residential development (retail development, industrial development, acres of office land use, and so forth). It goes without saying that the global financial crisis and housing bubble had an impact on the forecasts during this period. Although the forecasts developed in the early 2000s were probably not as bullish as the forecasts developed just a couple years later, the forecasts certainly do not reflect the economic impact felt from the economic highs in early 2006 through the recessionary impacts to 2012. Land use model inputs for the traffic and revenue models were based on the SANDAG Series 9 forecasts, adjusted in the forecasting process to meet the needs of the corridor. Comprehensive data from SANDAG comparing the base Series 9 forecasts to the current SANDAG Series 13 forecasts was not possible due to lack of data availability. However, data was available to compare observed (and estimated 2018–2020) San Diego County population estimates to the San Diego County household SANDAG model inputs. Figure III-H-13 compares San Diego County’s total number of households from the project planning study and compares the forecasts to actual population values for San Diego County, indexed so that 100 represents the index value of each variable in 2000. The chart shows that from 1990–2000 (which is observed data in both data sets), the indexed households and population track very well, as one would expect with consistent household sizes.5 The observed population data show that the slowdown in population growth from 2002–2006 was not reflected in the model input household forecasts. This slowdown abated slightly, but growth rates remained below annual growth forecasts until 2011 and never exceeded the projected household 5 Note the number of households is shown in 5-year increments and interpolated for the intermediate years where as the population data (from the St. Louis Federal Reserve FRED database is annual).

Appendix H: Deep Dives III-H-53 growth rates, indicating that the model input household forecasts were too high. This difference in household growth rates impacts the trip-generation component of the traffic model and the overall trip rates. Figure III-H-13. Comparison of observed and projected population and household growth in San Diego County. Housing Prices In addition to total population and households, housing prices can have an impact on perceptions of the localized economy and on values of time. The housing crisis impacted San Diego and the South Bay and Chula Vista areas in terms of both foreclosures and housing prices. Foreclosures, measured in changes in homeownership, were particularly devastating in the South Bay area, with the number of owner–occupied houses dropping significantly in the SBX corridor, as seen in Figure III-H-14 (Giuliano).

III-H-54 Traffic Forecasting Accuracy Assessment Research Figure III-H-14. Map of change in owner- occupied housing units in San Diego County. Figure III-H-15 (next page) shows the San Diego Home Price Index (S&P), which hit its maximum in March 2006 at 251.7 and fell to 145.4, even with 2002 levels, by May 2009. Since the low point of the recession, the home price index has grown, returning to pre-recession levels in early 2018. Since 1970, there have been five recessions (1973–1975, 1980–1982, 1990–1991, 2001, and 2008–2009). No forecast can accurately account for specific economic slowdowns or recessions. Exogenous socioeconomic forecasts are developed with economic cycles in mind, accounting for the impact of the cycle over the long term. Forecasters need to be aware of the potential short-term impacts of the economic cycle on traffic, ridership, and revenue forecasts for transportation projects. Public-Private Partnership in California, Phase II Report, Section VII: “California Political Environment,” July 2012 (Giuliano et al.)

Appendix H: Deep Dives III-H-55 Source: S&P Economic Research Division, Federal Reserve Bank of St. Louis Figure III-H-15. San Diego Home Price Index, 1987–2018. Border Crossings One driver of traffic on the SBX is the number of border crossings, given the proximity of the SBX to the Otay Mesa Border Crossing. The SBX T&R model used forecasts developed in the San Diego Region–Baja California Cross-Border Transportation Study from November 2000. This study projected a 25-year compound annual growth rate (1995 to 2020) of 3.2% per annum (p.a.) for passenger cars and 2.8% per annum (p.a.) for trucks. This volume compares reasonably well to the observed U.S.– Mexico border crossings reported by the Bureau of Transportation Statistics, which shows a compound annual growth rate of 4.4% p.a. for passenger cars and 2.7% p.a. for trucks. Although the overall border crossings forecasts have been close to observed growth for trucks and slightly underestimated for autos, the project report only presents the 1995 base and 2020 forecasts. The traffic and revenue models interpolated the intermediate years. Figure III-H-16 and Figure III- H-17 show the observed versus modeled auto and truck border crossing statistics and demonstrate the danger of interpreting long-term growth rates through intermediate forecast years. The long-term Figure III-H-16. U.S.– Mexico historical border crossings at Otay Mesa (passenger cars).

III-H-56 Traffic Forecasting Accuracy Assessment Research growth has been reasonable (and actually was underestimated for autos), but in the short term, growth has varied considerably, causing risk to intermediate traffic forecasts. Figure III-H-17. U.S.–Mexico historical border crossings at Otay Mesa (trucks). The hypothesis that the traffic and revenue forecasts were overestimated due to the inaccuracy of the border crossing forecasts could have validity for autos in 2010, which were 32% below the forecast value. Since that low point in border crossings, passenger car border crossings have grown in number and now exceed the forecast by over 20%. Truck forecasts, while reflecting the changing economic conditions, have remained within 10% of the forecast value since 2000. Traffic and Revenue Forecasts There is no consistent format of forecast or observed traffic on the SBX. Some reports refer to daily or annual transactions, others to ADT, while others report average weekday traffic (AWDT). There is an overall lack of observed transactional or traffic count data from opening to current conditions. Revenue data also is provided in different forms and varies greatly based on assumed toll rates, which vary from the project development to the several rounds of reforecasts with different toll policies tested. Figure III-H-18 shows the annual toll revenue forecast for the different analyses and observed toll revenue from the 2 fiscal years that are publicly available. As the chart clearly shows, the more recent toll revenue forecasts are much less aggressive than the project development and 2008 forecasts, which—in addition to having higher toll rates—did not project or fully appreciate the impact of the housing bubble and the global financial crisis on transportation.

Appendix H: Deep Dives III-H-57 Figure III-H-18. Annual revenue forecasts on the SBX. 5.5 Discussion This deep dive is not intended to be a criticism of the forecasts that were developed in 2003. Hindsight can allow us to perceive the warning signs, but very few people who saw the weakening of the housing bubble in 2007 recognized that a global financial crisis would follow in 2008. The SBX’s Transportation Infrastructure Finance and Innovation Act (TIFIA) program risk analysis report showed that the early-year project development forecasts had a probability of less than 5%, and that this included only risks associated with toll revenues (projections of construction costs and operating costs were held constant)—but the U.S.DOT certainly did not forecast the impending global financial crisis. If a more conservative approach had been taken in the development of the project, it is unlikely that a P3 would have found this an appropriate project. At the least, the concessionaire would have structured the deal differently. Researching through multiple forecasts and comparable data for this project, one clear recommendation emerged: during the forecast period, it is important for every project to develop clear model performance metrics that can be checked against observed data. Much like the data collected for transit before-and-after studies, these data could provide clear insight to the forecasting process and could be used in each region (and collectively in the United States) to understand common forecasting errors. These metrics may include: • Socioeconomic variables such as population and employment at sub-regional levels (focusing on the project corridors); • Regional VMT values and vehicle-hours traveled (VHT) values; • Consistent ADT measures as specific points in the corridor (e.g., a plan to collect annual traffic counts on the facility for the first 5–10 years of opening); and

III-H-58 Traffic Forecasting Accuracy Assessment Research Consistent definitions of other measures to be collected and maintained. For toll facilities this could be annual or daily transactions, revenue miles traveled, daily or annual revenue, average toll rates, and so forth. 6 US-41, Brown County, Wisconsin 6.1 Introduction The US-41 project in Brown County, Wisconsin, is a project of capacity addition involving the reconstruction of nine interchanges, construction of 24 roundabouts, addition of collector-distributer lanes, and building of two system interchanges. This report, written in April 2018, assesses the accuracy of traffic forecasts for the US-41 project in Brown County. The analysis focuses on investigating an approximately 3.3-mile segment of observed pre-/post-construction traffic counts and traffic forecasts, presented in the FEIS for US-41 Memorial Drive to County M, Brown County, Wisconsin. The FEIS provided traffic forecasts for four sites. Traffic forecasts were prepared in 2011 for the 2015 (construction-year) and 2035 (design- year) forecasts. The segment of the US-41 project for this report opened in the spring of 2017. Traffic counts were available for the 2009–2017 year. Compared to the other deep dive analysis cases, the US- 41 project was, to some extent, an imperfect deep dive given the limited availability of traffic forecasts, counts, and exogenous forecast data such as employment forecasts. This report consists of six sections. Section 6.2 describes the project. Section 6.3 compares the predicted and actual traffic volumes for all roadways in the study area where post-opening traffic counts were available. Section 6.4 enumerates the exogenous forecasts and sources of forecast error for the project. It also includes an assessment of the accuracy of the exogenous forecasts. Section 6.5 attempts to identify items discussed in Section 6.4 that were important sources of forecast error and, for the items so identified, attempts to quantify how much it would change the forecast if the forecasters had accurate information about the item. Section 6.6 summarizes the findings. 6.2 Project Description The US-41 project in Brown County aimed to improve safety and road capacity by replacing old and deteriorating pavement and outdated design infrastructure with new standards. The project area was an approximately 14-mile portion of US-41 in Brown County, Wisconsin, that covered US- 41 from Orange Lane near the County Road F interchange to the County Road M interchange. Figure III-H-19 shows the area of the project, along with five roadway segments. For this deep dive, however, the study area is only the 3.3-mile roadway covered by the FEIS of the Memorial Drive to County M segment (Segment 5 in Figure III-H-19). Out of the five segments, the Memorial Drive to County M segment is the only segment that required an EIS. This segment’s potential environmental impacts related to the building of two system interchanges at WIS-29 and I-43 with tall flyover-type ramps to be built on top of swampland. The other four segments in Figure III-H-19 underwent an increase from 4 lanes to 6 lanes (or 8 lanes, in the cases of some segments that included auxiliary lanes). For these segments, it was necessary to re- evaluate the original environmental Assessment (EA) document that had been completed in 2002 or complete a new EA, but a full EIS was not required.

Appendix H: Deep Dives III-H-59 Source: Final EIS, US-41 Memorial Drive to County M, Brown County, Wisconsin (Wisconsin DOT Project ID 1133-10-01), ftp://ftp.dot.wi.gov/dtsd/bts/environment/library/1133-10-01-F.pdf Figure III-H-19. Project study area (US-41, Brown County). The US-41 project in Brown County was part of a 31-mile US-41 highway reconstruction project in Winnebago County and Brown County. The project areas in the two counties were not all connected along US-41 as seen in Figure III-H-20; rather, they were located on portions of US-41 that were adjacent to two major cities—Green Bay, in Brown County, and Oshkosh, in Winnebago County. The US-41 project was the largest reconstruction project in the history of the Northeast Region in Wisconsin.

III-H-60 Traffic Forecasting Accuracy Assessment Research Source: Map Data: Google Maps, annotated by NCHRP 08-110 project team Figure III-H-20. Areas of US-41 project in Brown County and Winnebago County. Construction of the 17-mile Winnebago County portion was completed in 2014 with a $450 million budget. This portion of the project did not involve any heavy construction work as the counterpart did for two new system-wide interchanges at WIS-29 and I-43. The Brown County portion of the project took 6 years to complete, from 2011 to 2017, and cost approximately $1 billion. Figure III-H-21 compares key numbers related to the two portions of this project. The new traffic lanes and bridges were expected to last for 50–75 years. Source: Project Fact Sheet, us41.wisconsin.gov Figure III-H-21. US-41 project by numbers.

Appendix H: Deep Dives III-H-61 Source: Wisconsin DOT Figure III-H-22. A map of Wisconsin DOT regions and Fox Valley area (within a red boundary). The US-41 project was important to Wisconsin and the region because it upgraded a transportation link that supported important economic vitality in the Fox River Valley between population and most of its workforce and manufacturing facilities. Figure III-H-22 shows maps depicting the Wisconsin DOT’s planning regions and the Fox Valley area. 6.3 Predicted–Actual Comparison of Traffic Forecasts There were four links/roadways in this deep dive study area (as seen in Figure III-H-23). For the four links, the FEIS for the US-41 Memorial Drive to County M in Brown County6 provided an existing-year traffic count, a 2005 count, and two future-year forecasts (for 2015 and 2035). The traffic forecasts were model outputs from a regional travel demand model at the Brown County Planning Commission (BCPC), a regional MPO. Traffic volumes were expressed as ADT volumes, which reflect average travel conditions rather than daily or seasonal fluctuations. 6 Final EIS—US-41 Memorial Drive to County M, Brown County, Wisconsin (Wisconsin DOT Project ID 1133-10-01); ftp://ftp.dot.wi.gov/dtsd/bts/environment/library/1133-10-01-F.pdf southeastern and northeastern Wisconsin—two areas that contain more than half the state’s

III-H-62 Traffic Forecasting Accuracy Assessment Research Sources:: Wisconsin DOT Figure III-H-23. Traffic count locations in the US-41 study area. Of the four links, three links had ADT traffic counts that were publicly available as of June 2018. The following table lists each of these links with their forecast and observed ADT counts. The table includes an inaccuracy index in traffic forecasts that was estimated as: = − Table III-H-20 shows that the traffic forecasts for the study sites were generally accurate, with the percent difference from forecast ranging from -10% to -3% for three study sites. Table III-H-20. Existing and forecast traffic (2005–2035) from US-41 traffic study and EIS. Site ID Site Segment Base Year Opening- Year Count Opening- Year Forecast Percent Difference from Forecast 2005 2017 2017 1 US-41 Mainline, STH-29 to Velp Ave 61,200 71,547 73,400 -2.52% 2 US-41 Mainline, Velp Ave. to I-43 56,800 69,300 3 US-41 Mainline, I-43 to Lineville Rd 50,200 54,300 60,300 -9.95% 4 I-43, Atkinson Drive to US-41 38,400 42,881 44,200 -2.98% Notes: Preliminary ADT, 06/06/2017 on the Roadrunner webpage: https://trust.dot.state.wi.us/roadrunner/. According to the Wisconsin DOT, a preliminary ADT is generated when the raw count is first processed, using factors based on continuous data from the previous year. Part of the annual processing of all traffic count data is the generation of new factors based on current year continuous data. These current year factors are then applied to all of the short-term counts taken during the year to compute a final ADT for each site (http://wisconsindot.gov/Documents/projects/by- region/ne/23exp/23ls-a.pdf). (III-H-1)

Appendix H: Deep Dives III-H-63 6.4 Potential Sources of Forecast Error This section identifies the exogenous forecasts and project assumptions used in the development of the traffic forecasts. Exogenous forecasts are made outside of the immediate traffic forecasting process. Project assumptions are established during project development and serve as the basis for the traffic forecast. Population and employment forecasts are examples of exogenous forecasts, and they are leading sources of forecast error. These forecasts are usually made by outside planning agencies on a regular basis; that is, they are not prepared for any individual project. During project development, these forecasts are revised to match the specific assumptions documented by the project team. Past forecasting research has identified several exogenous forecasts and project assumptions as common sources of forecast error, including: Macroeconomic conditions (of the region or study area), Population and employment forecasts, Significant changes in land use, Auto fuel prices, Tolling pricing, sensitivity and price levels, Auto ownership, Changes in technology, Travel times within the study area, and Duration between year forecast produced and opening year. Table III-H-21 lists all the exogenous forecasts and project assumptions for which observed data were available for the US-41 project. It also includes an assessment of the accuracy of each item. Table III-H-21. List of exogenous forecasts and project assumptions (US-41 project). Items Quantifiable Observed Year 2010 Values Estimated Year 2010 Values Percent Difference from Forecast Population * Yes 135,897 138,775 -2% Auto Fuel Price (price per gallon) ** Yes $2.41 $2.73 -12% Study-Forecast Duration Yes 12 10 20% Data Sources for Observed Values: * Sum of population in City of Green Bay, Village of Howard and Town of Suamico; FEIS, US-41 Memorial Drive to County M in Brown County (ftp://ftp.dot.wi.gov/dtsd/bts/environment/library/1133-10-01-F.pdf) ** BLS, Office of Energy Efficiency (CPI-All Urban Consumers (Current Series), All items in Boston-Cambridge-Newton, MA-NH, all urban consumers, not seasonally adjusted, https://www.bls.gov/data/) & Renewable Energy & EPA (New England (PADD 1A) All Grades All Formulations Retail Gasoline Prices (Dollars per Gallon) for year 2010; https://www.eia.gov/dnav/pet/pet_pri_gnd_dcus_YBOS_w.htm) The NCHRP 08-110 project team had difficulties finding available data sources for the US-41 deep dive analysis. Only the exogenous forecast data and project assumptions noted in Table III-H- 21 were identified for the potential sources of forecast errors for the US-41 project.

III-H-64 Traffic Forecasting Accuracy Assessment Research Table III-H-21 shows that the population forecast was close to observed population, auto fuel prices were slightly overestimated, and the opening year was delayed by 2 years. Information on other typical exogenous forecasts (e.g., macroeconomic conditions, car ownership, travel time, and value of time) was unavailable publicly. For fuel price, a proxy fuel price forecast was estimated by multiplying the 2005 average gasoline price with an annual inflation rate between 2005 and 2017. 6.5 Sources Contributing to Forecast Error Building upon the items discussed in Section 6.4, this section attempts to identify items that are important sources of forecast error and, for those items so identified, attempts to quantify how much it would change the forecast if the forecasters had accurate information about the item. Adjusted forecasts for the critical roadways were computed by applying an elasticity to the relative change between the actual and predicted values for each item in Section 6.4. Only those items which could be quantified and deemed important for this project were adjusted. The effect on the forecast can be quantified in this way. First, the change in forecast value, a delta between the opening- year forecast and the actual observed traffic count in the opening year, is calculated. Change in Forecast Value = ( − ) ( )⁄ Second, a factor of the effect on forecast is calculated by exponentiating an elasticity of the common source errors, and a natural-log of the change rate in forecast value is calculated. This factor is then applied to the actual forecast volume to generate an adjusted forecast. Effect on Forecast = ( ∗ (1+ ℎ )) − 1 Adjusted Forecast = (1 + ) ∗ This deep dive analysis adopted the best elasticity values possible based on those identified by Ewing et al. (2014) via their cross-sectional and longitudinal models together, and from other transportation literature (Dong et al. 2012; Dunkerley, Rohr, and Daly 2014). It is important to note that the elasticity values identified by Ewing et al. (2014) were related to VMT, not traffic volumes. To the best of our knowledge and the literature review, there is no literature investigating elasticity values for traffic volumes with respect to employment, population, and fuel price. Also, none of the literature reviewed discussed elasticity values of VMT, nor traffic volume, with respect to employment. To this end, the elasticity study in NCHRP 08-110 reflects two assumptions: (1) the elasticity values of VMT with respect to population and fuel price are close to the elasticity values of traffic volumes given a high correlation between VMT and traffic volumes, and (2) the elasticity values regarding employment are close to the ones for per capita income, again because of their high correlation. The elasticity values used in the US-41 study were: 0.75 for population, and 0.2 for fuel price. The results of quantifying the effect on the forecast are shown in Table III-H-22. The original forecast value was successively adjusted for each of the items identified as contributing sources of (III-H-2) (III-H-3) (III-H-4)

Appendix H: Deep Dives III-H-65 forecasting error for all the segments. The final remaining percentage differences after all adjustments are shown in Table III-H-22. The segment IDs reflect the sites shown in Figure III-H-19. Table III-H-22. Forecast adjustment table based on elasticities. Seg# Items Actual Value Forecast Value Change in Forecast Value Elasticity Effect on Forecast Actual Forecast Volume Adj. Forecast Volume Remaining Percent Difference from Adj. Forecast 3 Population 135,897 138,775 -2% 0.75 -2% 60,300 59,360 9% 3 Fuel Price 2.41 2.73 -0.12 -0.2 3% 59,360 60,893 12% 3 Original Traffic Forecast 54,300 60,300 -10% N/A N/A 3 Adjusted Traffic Forecast N/A N/A N/A 60,300 60,893 -11% 1 Population 135,897 138,775 -2.07% 0.75 -2% 73,400 72,255 1% 1 Fuel Price 2.41 2.73 -0.12 -0.2 3% 72,255 74,122 4% 1 Original Traffic Forecast 71,547 73,400 -3% N/A N/A 1 Adjusted Traffic Forecast N/A N/A N/A 73,400 74,122 -3% 4 Population 135,897 138,775 -2% 0.75 -2% 44,200 43,511 1% 4 Fuel Price 2.41 2.73 -0.12 -0.2 3% 43,511 44,635 4% 4 Original Traffic Forecast 42,881 44,200 -3% N/A N/A 4 Adjusted Traffic Forecast N/A N/A N/A 44,200 44,635 -4% Total Population 135,897 138,775 -2% 0.75 -2% 177,900 175,126 4% Fuel Price 2.41 2.73 -0.12 -0.2 3% 175,126 179,650 6% Original Traffic Forecast 168,728 177,900 5% N/A N/A Adjusted Traffic Forecast N/A N/A N/A 177,900 179,650 -6% In general, the adjustments resulted in slightly negative impacts on traffic forecast accuracy. All three study sites experienced increases in the forecast percent difference from forecast (by 1% to 2%); however, the cumulative impacts of each factor disclosed that the population adjustment resulted in improved traffic forecast accuracy. The overall negative impacts came from the fuel price adjustment. 6.6 Discussion The US-41 project mainly increased highway lanes from 4 lanes to 6 lanes (or 8 lanes, in the cases of some segments that included auxiliary lanes). This project replaced old and deteriorating pavement and outdated design infrastructure, which resulted in reconstruction of nine interchanges, construction of 24 roundabouts, the addition of collector-distributer lanes, and the building of two system interchanges. The project was intended to improve safety and upgrade a transportation link that supports economic vitality in the region between southeastern and the northeastern Wisconsin. The original traffic forecasts were slightly overestimated (by 3% to 10%) for three study sites, but they were generally close. It should be noted that the traffic count for Site 3 was the preliminary ADT, not the final ADT. The highest delta between the traffic forecast and the opening-year count for Site 3 may derive from the usage of the preliminary estimate. The traffic forecasting accuracy improved after correcting the exogenous population forecast. However, the fuel price adjustment increased the percent difference from forecast. This change could have been accounted for in that the change in fuel price had little effect on the traffic volumes in the

III-H-66 Traffic Forecasting Accuracy Assessment Research A small number of documents and data were available for the US-41 project. It is unknown whether risk and uncertainty were considered during the project due to the inaccessibility of the documentation on this project. For future forecasting efforts, it is suggested that a copy of the forecasting documentation and assumptions be archived along with the travel model files used to generate the forecasts. study area where public transportation is not a reasonable alternative mode. This interpretation could be wrong, however, because of the uncertainty in how the fuel price impact was implemented in the traffic forecast model. Availability of the archived model and its inputs would have provided deeper understanding of the parameters and methods used for forecasting traffic for the US-41 project.

Appendix H: Deep Dives III-H-67 References American Automobile Association (2013). “Your Driving Costs: How Much Are You Really Paying to Drive?” http://exchange.aaa.com/wp-content/uploads/2013/04/Your-Driving-Costs-2013.pdf. Dong, J., D. Davidson, F. Southworth, and T. Reuscher (2012). “Analysis of Automobile Travel Demand Elasticities With Respect To Travel Cost.” Prepared for the Federal Highway Administration by Oak Ridge National Lab. Dunkerley, F., C. Rohr, and A. Daly (2014). “Road Traffic Demand Elasticities: A Rapid Evidence Assessment,” Crown Copyright, RR-888-DFT, available at https://www.rand.org/pubs/ research_reports/RR888.html (accessed February 14, 2020). Ewing, R., S. Hamidi, F. Gallivan, A. C. Nelson, and J. B. Grace (2014). “Structural Equation Models of VMT Growth in U.S. Urbanised Areas.” Urban Studies, 51(14): 3079–96. https://doi.org/10.1177/0042098013516521. Additional Sources, Organized by Deep Dive Eastown Road Extension, Lima, Ohio 1. AAA (2013) Driving Costs Report: http://exchange.aaa.com/wp-content/uploads/2013/04/Your- Driving-Costs-2013.pdf. 2. Dong et. al. (n.d.) Analysis of Automobile Travel Demand Elasticities With Respect To Travel Cost. Prepared for FHWA: https://www.fhwa.dot.gov/policyinformation/pubs/hpl-15-014/ TCElasticities.pdf. 3. Dunkerley et. al. (2014). Road traffic demand elasticities A rapid evidence assessment. RAND Europe : https://www.rand.org/content/dam/rand/pubs/research_reports/RR800/RR888/RAND_RR888.pdf. 4. Ewing et al. (2014). Structural equation models of VMT growth in US urbanised areas. Urban Studies 2014, Vol. 51(14) 3079–3096 https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article= 1910&context=usgsstaffpub. 5. Opening Year Counts: https://www.dot.state.oh.us/Divisions/Planning/TechServ/traffic/ Pages/TMMS.aspx. 6. Opening Year Forecasts: CUBE model network obtained by re-running opening year scenario. Indian Street Bridge, Palm City, Florida 1. Indian Street Bridge PD&E, Design Traffic Technical Memorandum, FDOT, January 23, 2003. 2. SR-714 and Martin Highway/Indian Street (Indian Street Bridge Crossing), Revised Traffic Projection and Turning Movement Report, Calvin. Giordano & Associates, Inc., September 20, 2020. 3. Dunkerley et. al. (2014). Road Traffic Demand Elasticities: A Rapid Evidence Assessment. RAND Europe : https://www.rand.org/content/dam/rand/pubs/research_reports/ RR800/RR888/RAND_RR888.pdf. 4. Ewing et al. (2014). Structural Equation Models of VMT Growth in U.S. Urbanised Areas. Urban Studies 2014, 51(14) 3079–3096: https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article =1910&context=usgsstaffpub. 5. Opening Year Counts: Martin County 2014 Roadway Level of Service Inventory Report.

III-H-68 Traffic Forecasting Accuracy Assessment Research 6. Opening Year Forecasts: Treasure Coast Regional Planning Model (TCRPM II) 2025 Cost Feasible Model (A25). 7. BEBR Historical Data: https://www.bebr.ufl.edu/population/website-article/aging-florida. Central Artery Tunnel, Boston, Massachusetts 1. Central Artery/Tunnel Backcasting Study, Technical Memorandum, CTPS, October 15, 2014. 2. Ewing et al. (2014). Structural Equation Models of VMT Growth in U.S. Urbanised Areas. Urban Studies 2014, 51(14) 3079–3096: https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1910&context =usgsstaffpub. 3. Transportation Impacts of the Massachusetts Turnpike Authority and the Central Artery/Third Harbor Tunnel Project, Economic Development Research Group, Inc, February 2006. 4. Altshuler, A. A., and D. E. Luberoff (2002). Mega-Projects: The Changing Politics of Urban Public Investment. 5. The Big Dig: Tunnels and Bridges: https://www.mass.gov/service-details/the-big-dig-tunnels-and- bridges (accessed May 2, 2018). 6. The Big Dig: Project Background: https://www.mass.gov/service-details/the-big-dig-project- background (accessed May 2, 2018) 7. The Central Artery/Third Harbor Tunnel Project (“The Big Dig”), UCL Bartlett School of Planning: http://www.omegacentre.bartlett.ucl.ac.uk/wp-content/uploads/2014/12/USA_BIGDIG_ PROFILE.pdf. 8. Boston Big Dig, Central Artery/Tunnel Project, Massachusetts: https://www.roadtraffic- technology.com/projects/big_dig/ (accessed on May 7, 2018). 9. Boston Herald (2015). “More Than 1500 Faulty Nuts Discovered in Big Dig Tunnels,” http://www.bostonherald.com/news/local_coverage/2015/11/more_than_1500_faulty_nuts_ discovered_in_big_dig_tunnels (accessed May 7, 2018). 10. Boston Globe (2015). “10 Years Later, Did the Big Dig Deliver?” December 29, 2015: https://www.bostonglobe.com/magazine/2015/12/29/years-later-did-big-dig-deliver/tSb8PIMS4 QJUETsMpA7SpI/story.html (accessed May 7, 2018). 11. A History of Central Artery/Tunnel Project Finances 1994–2001: Report to the Treasurer of the Commonwealth, March 20, 2001: https://www.mass.gov/files/documents/2016/08/xa/cat01rpt.pdf (accessed May 11, 2018). 12. CTPS Express Highway Volumes, I-93/Central Artery Between Columbia Road, Dorchester, and Route 1, Charlestown: ftp://ctps.org/pub/Express_Highway_Volumes/20_I93_Central_Artery.pdf (accessed May 11, 2018). Cynthiana Bypass, Cynthiana, Kentucky 1. Kentucky Transportation Cabinet (KYTC). Cynthiana Urban Area Transportation Study, January 1990. 2. KYTC. Cynthiana Urban Area Transportation Study Technical Document, January 1989. 3. Map showing location of Cynthiana Bypass. Google Earth, earth.google.com/web/. 4. U.S. Census Bureau, Census 2010 Summary File 1, Tables P5, P6, P8, P12, P13, P17, P19, P20, P25, P29, P31, P34, P37, P43, PCT5, PCT8, PCT11, PCT12, PCT19, PCT23, PCT24, H3, H4, H5, H11, H12, and H16. South Bay Expressway, San Diego, California 1. CalTrans (2016). Transportation Concept Report State Route 125, District 11, February 2016: http://www.dot.ca.gov/dist11/departments/planning/pdfs/tcr/2016_TCR_SR_125.pdf. 2. Giuliano, G., et al. (2012). Public-Private Partnerships in California, Phase II Report, Section VII: California Political Environment, July 2012.

Appendix H: Deep Dives III-H-69 3. Hatch Mott McDonald (2008). South Bay Expressway Traffic and Revenue Forecasts Report to Lenders, December 2008. 4. Hatch Mott McDonald (2010). South Bay Expressway Forecast Review Stage 2, March 2010. 5. Hatch Mott McDonald, South Bay Expressway Forecast Review Update Stage 2, September 2010 6. Louis Berger Group, Inc. (2003). Due Diligence Analysis for the San Diego SR125 Expressway Traffic and Revenue Study, January 2003. 7. Maquarie Infrastructure Group. Revenue and Traffic Statistics, 2nd Quarter—Financial Year 2010. 8. Maquarie Infrastructure Group. Traffic Statistics, December Quarter 2008 and Financial Year 2009 to Date: imagesignal.comsec.com.au/asxdata/20090717/pdf/00969370.pdf. 9. S&P Dow Jones Indices LLC (2018.). S&P/Case-Shiller CA-San Diego Home Price Index [SDXRSA], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/SDXRSA, (accessed July 12, 2018). 10. Stantec, Memo: SR 125 Preliminary Review, February 25, 2011 11. Stantec, Memo: SR 125 Traffic and Revenue Summary, April 19, 2011 12. Stantec, Memo: Comparison of Stantec Versus HMM Assumptions, June 28, 2011 13. Stantec, Memo: SR 125 Certification Letter Supporting Material, April 6, 2012 14. TIFIA Joint Program Office, USDOT (2013). S.R. 125 South Toll Road Project Risk Analysis of Traffic and Toll Revenue Forecasts Draft Final Report, June 20, 2003. 15. Wilbur Smith Associates (2002). Traffic and Revenue Study Proposed S.R. 125 South Tollway, October 2002. US-41, Brown County, Wisconsin 1. Final EIS: US-41 Memorial Drive to County M, Brown County, Wisconsin (WisDOT Project I.D. 1133- 10-01): ftp://ftp.dot.wi.gov/dtsd/bts/environment/library/1133-10-01-F.pdf. 2. USH-41 Traffic Study—Brown County Forecasted Traffic Volume Network, January 2, 2007 by CH2M HILL (report received from Chris Chritton, Wisconsin DOT). 3. WFRV-TV (2016). “41 Project Construction in Green Bay Area Enters Final Year,” January 1, 2016: http://www.wearegreenbay.com/news/local-news/41-project-construction-in-green-bay-area-enters- final-year/316454453 (accessed 4/18/2018). 4. Connecting Wisconsin, US 41 Project by the Numbers, 2014-08-21. 5. Fox Valley wisconsin map, https://bnhspine.com/fox-valley-wisconsin-map.html. 6. Wisconsin Hourly Traffic Data Index Page. Wisconsin Traffic Operations and Safety Laboratory, The WisTransPortal System: http://transportal.cee.wisc.edu/products/hourly-traffic- data/bysiteid/brown.html. 7. Wisconsin DOT Interactive Traffic Count Map: https://trust.dot.state.wi.us/roadrunner/. 8. BLS. CPI-All Urban Consumers (Current Series), All items in Boston-Cambridge-Newton, MA-NH, All Urban Consumers, Not Seasonally Adjusted: https://www.bls.gov/data/. 9. EPA. New England (PADD 1A) All Grades All Formulations Retail Gasoline Prices (Dollars per Gallon) for Year 2010: https://www.eia.gov/dnav/pet/pet_pri_gnd_dcus_YBOS_w.htm. 10. Ewing et al. (2014). Structural Equation Models of VMT Growth in U.S. Urbanised Areas. Urban Studies 2014, 51(14), 3079–3096: https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1910&context =usgsstaffpub. 11. Dunkerley et. al. (2014). Road Traffic Demand Elasticities: A Rapid Evidence Assessment. RAND Europe: https://www.rand.org/content/dam/rand/pubs/research_reports/RR800/RR888/RAND_RR888.pdf. 12. Dong et. al. Analysis of Automobile Travel Demand Elasticities With Respect To Travel Cost, prepared for FHWA: https://www.fhwa.dot.gov/policyinformation/pubs/hpl-15-014/TCElasticities.pdf.

Abbreviations and acronyms used without definitions in TRB publications: A4A Airlines for America AAAE American Association of Airport Executives AASHO American Association of State Highway Officials AASHTO American Association of State Highway and Transportation Officials ACI–NA Airports Council International–North America ACRP Airport Cooperative Research Program ADA Americans with Disabilities Act APTA American Public Transportation Association ASCE American Society of Civil Engineers ASME American Society of Mechanical Engineers ASTM American Society for Testing and Materials ATA American Trucking Associations CTAA Community Transportation Association of America CTBSSP Commercial Truck and Bus Safety Synthesis Program DHS Department of Homeland Security DOE Department of Energy EPA Environmental Protection Agency FAA Federal Aviation Administration FAST Fixing America’s Surface Transportation Act (2015) FHWA Federal Highway Administration FMCSA Federal Motor Carrier Safety Administration FRA Federal Railroad Administration FTA Federal Transit Administration HMCRP Hazardous Materials Cooperative Research Program IEEE Institute of Electrical and Electronics Engineers ISTEA Intermodal Surface Transportation Efficiency Act of 1991 ITE Institute of Transportation Engineers MAP-21 Moving Ahead for Progress in the 21st Century Act (2012) NASA National Aeronautics and Space Administration NASAO National Association of State Aviation Officials NCFRP National Cooperative Freight Research Program NCHRP National Cooperative Highway Research Program NHTSA National Highway Traffic Safety Administration NTSB National Transportation Safety Board PHMSA Pipeline and Hazardous Materials Safety Administration RITA Research and Innovative Technology Administration SAE Society of Automotive Engineers SAFETEA-LU Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (2005) TCRP Transit Cooperative Research Program TDC Transit Development Corporation TEA-21 Transportation Equity Act for the 21st Century (1998) TRB Transportation Research Board TSA Transportation Security Administration U.S. DOT United States Department of Transportation

TRA N SPO RTATIO N RESEA RCH BO A RD 500 Fifth Street, N W W ashington, D C 20001 A D D RESS SERV ICE REQ U ESTED N O N -PR O FIT O R G . U .S. PO STA G E PA ID C O LU M B IA , M D PER M IT N O . 88 Traffic Forecasting A ccuracy A ssessm ent Research TRB ISBN 978-0-309-48143-4 9 7 8 0 3 0 9 4 8 1 4 3 4 9 0 0 0 0