Traffic Forecasting Accuracy Assessment Research (2020) / Chapter Skim
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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
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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
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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 northsouth arterial is located on the western edge of the city of Lima in Allen County, Ohio.
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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.
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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.
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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.
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Appendix H: Deep Dives III-H-7 Table III-H-2. Input accuracy assessment table (Eastown Road Extension)
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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 = ( − )
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Table III-H-3. Forecast adjustment table based on elasticities for all segments (Eastown Road Extension)
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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.
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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.
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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.
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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.
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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)
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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.
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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.
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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)
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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)
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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.
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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.
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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.
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Table III-H-7. Forecast adjustment table based on elasticities for all segments (Indian Street Bridge project)
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Table III-H-7 (Continued)
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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.
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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.
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III-H-26 Traffic Forecasting Accuracy Assessment Research Table III-H-9. Adjusted forecast table using the model (Indian Street Bridge project)
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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.
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III-H-28 Traffic Forecasting Accuracy Assessment Research Table III-H-10. External trip distribution using both competing bridges.
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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)
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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.
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Appendix H: Deep Dives III-H-31 retrieved from the 1991 Final Supplemental Environmental Impact Report [FSEIR]
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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.
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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)
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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.
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Table III-H-13. Forecast adjustment table based on elasticities (CA/T project)
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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.
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Table III-H-13 (Continued)
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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.
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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.
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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.
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Appendix H: Deep Dives III-H-41 Table III-H-14. Availability of data for Cynthiana Bypass project.
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III-H-42 Traffic Forecasting Accuracy Assessment Research shows the comparison of external–external (EE) and external–internal (EI)
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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.
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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.
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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.
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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.
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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.
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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)
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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.
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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.
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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.
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III-H-52 Traffic Forecasting Accuracy Assessment Research Figure III-H-12. Model and actual full-length ETC tolls on SBX.
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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.
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III-H-54 Traffic Forecasting Accuracy Assessment Research Figure III-H-14. Map of change in owneroccupied housing units in San Diego County.
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Appendix H: Deep Dives III-H-55 Source: S&P Economic Research Division, Federal Reserve Bank of St. Louis Figure III-H-15.
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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.
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Appendix H: Deep Dives III-H-57 Figure III-H-18. Annual revenue forecasts on the SBX.
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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.
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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.
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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.
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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)
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III-H-62 Traffic Forecasting Accuracy Assessment Research Sources:: Wisconsin DOT Figure III-H-23. Traffic count locations in the US-41 study area.
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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.
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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)
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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.
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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.
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Appendix H: Deep Dives III-H-67 References American Automobile Association (2013)
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III-H-68 Traffic Forecasting Accuracy Assessment Research 6. Opening Year Forecasts: Treasure Coast Regional Planning Model (TCRPM II)
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Appendix H: Deep Dives III-H-69 3. Hatch Mott McDonald (2008)
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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)
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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 .
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