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Traffic Forecasting Accuracy Assessment Research (2020)

Chapter: Appendix C - Deep Dive Annotated Outline

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Suggested Citation:"Appendix C - Deep Dive Annotated Outline." National Academies of Sciences, Engineering, and Medicine. 2020. Traffic Forecasting Accuracy Assessment Research. Washington, DC: The National Academies Press. doi: 10.17226/25637.
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Suggested Citation:"Appendix C - Deep Dive Annotated Outline." National Academies of Sciences, Engineering, and Medicine. 2020. Traffic Forecasting Accuracy Assessment Research. Washington, DC: The National Academies Press. doi: 10.17226/25637.
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Suggested Citation:"Appendix C - Deep Dive Annotated Outline." National Academies of Sciences, Engineering, and Medicine. 2020. Traffic Forecasting Accuracy Assessment Research. Washington, DC: The National Academies Press. doi: 10.17226/25637.
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Suggested Citation:"Appendix C - Deep Dive Annotated Outline." National Academies of Sciences, Engineering, and Medicine. 2020. Traffic Forecasting Accuracy Assessment Research. Washington, DC: The National Academies Press. doi: 10.17226/25637.
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Suggested Citation:"Appendix C - Deep Dive Annotated Outline." National Academies of Sciences, Engineering, and Medicine. 2020. Traffic Forecasting Accuracy Assessment Research. Washington, DC: The National Academies Press. doi: 10.17226/25637.
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Suggested Citation:"Appendix C - Deep Dive Annotated Outline." National Academies of Sciences, Engineering, and Medicine. 2020. Traffic Forecasting Accuracy Assessment Research. Washington, DC: The National Academies Press. doi: 10.17226/25637.
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Suggested Citation:"Appendix C - Deep Dive Annotated Outline." National Academies of Sciences, Engineering, and Medicine. 2020. Traffic Forecasting Accuracy Assessment Research. Washington, DC: The National Academies Press. doi: 10.17226/25637.
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Suggested Citation:"Appendix C - Deep Dive Annotated Outline." National Academies of Sciences, Engineering, and Medicine. 2020. Traffic Forecasting Accuracy Assessment Research. Washington, DC: The National Academies Press. doi: 10.17226/25637.
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Suggested Citation:"Appendix C - Deep Dive Annotated Outline." National Academies of Sciences, Engineering, and Medicine. 2020. Traffic Forecasting Accuracy Assessment Research. Washington, DC: The National Academies Press. doi: 10.17226/25637.
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Suggested Citation:"Appendix C - Deep Dive Annotated Outline." National Academies of Sciences, Engineering, and Medicine. 2020. Traffic Forecasting Accuracy Assessment Research. Washington, DC: The National Academies Press. doi: 10.17226/25637.
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III-C-1 Deep Dive Annotated Outline A P P E N D I X C 1 Introduction <Name of the project> is a <type of project [capacity addition, reconstruction, etc.]> located in <city, state>. This report, written in month-year, assesses the utility, reliability and accuracy of traffic forecasts for the <project name>. Traffic forecasts for the project were prepared in <YYYY> for the <YYYY>, <YYYY>, and <YYYY> forecast year(s). The project opened in <YYYY>. Traffic counts are available for the year(s) <YYYY–YYYY>, all post-opening. Section 2 describes the project. Section 3 compares the predicted and actual traffic volumes for all roadways in the study area where post-opening traffic counts are available. Section 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 5 attempts to identify items discussed in Section 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 6 summarizes the findings from the previous two sections. Section 7 discusses suggested improvements to the forecasting methods, forecasting practices, and/or validation practices to be used for future projects. Section 8 provides a list of data sources and references used in the development of this report. 2 Project Description The study area boundaries are <here>, <here>, <here>, and <here>. A summary of the project scope goes <here>. <Describe any unique characteristics of the project. Some examples include: first project of its type in the region, first project of its type in decades, and exceptional project length, construction period and/or cost.> <Include a map.> 3 Predicted-Actual Comparison of Traffic Forecasts There are <NN> links/roadways in the study area. Traffic forecasts were made for <NN> links, or <PP> percent. Describe generally how the traffic forecasts were produced (e.g., model outputs only, post-processed model outputs, traffic counts with growth rate [define growth rate], etc.). following table lists each link with its forecast and observed AADT. Slight differences between the forecast year and the year traffic forecasts were collected should be noted here. Here is an overall assessment of the accuracy of these forecasts. There are <NN> links, or <PP> percent, with an annual average daily traffic (AADT) count. The

Table III-C-1. Traffic volume accuracy assessment (columns in yellow require numerical input). Seg# Project Segment and Direction Time of Day Base Year Count Base Year Forecast (if different) Opening Year Count Opening Year Forecast Over- Estimating Rate % Growth in Count % Growth in Forecast Sources of Data Comments 1 AADT #DIV/0! #DIV/0! #DIV/0! 1 AM Peak #DIV/0! #DIV/0! #DIV/0! 1 PM Peak #DIV/0! #DIV/0! #DIV/0! 2 AADT #DIV/0! #DIV/0! #DIV/0! 2 AM Peak #DIV/0! #DIV/0! #DIV/0! 2 PM Peak #DIV/0! #DIV/0! #DIV/0! 3 AADT #DIV/0! #DIV/0! #DIV/0! 3 AM Peak #DIV/0! #DIV/0! #DIV/0! 3 PM Peak #DIV/0! #DIV/0! #DIV/0! 4 AADT #DIV/0! #DIV/0! #DIV/0! 4 AM Peak #DIV/0! #DIV/0! #DIV/0! 4 PM Peak #DIV/0! #DIV/0! #DIV/0! 5 AADT #DIV/0! #DIV/0! #DIV/0! 5 AM Peak #DIV/0! #DIV/0! #DIV/0! 5 PM Peak #DIV/0! #DIV/0! #DIV/0! 6 AADT #DIV/0! #DIV/0! #DIV/0! 6 AM Peak #DIV/0! #DIV/0! #DIV/0! 6 PM Peak #DIV/0! #DIV/0! #DIV/0! 7 AADT #DIV/0! #DIV/0! #DIV/0! 7 AM Peak #DIV/0! #DIV/0! #DIV/0! 7 PM Peak #DIV/0! #DIV/0! #DIV/0! 8 AADT #DIV/0! #DIV/0! #DIV/0! 8 AM Peak #DIV/0! #DIV/0! #DIV/0! 8 PM Peak #DIV/0! #DIV/0! #DIV/0! Base Year Count: {Report Name/Website link/Model Run Details} Base Year Forecast: Opening Year Count: Opening Year Forecast: Note: Interactive versions of Tables III-C-1 through III-C-4 can be accessed in the Excel file titled “Deep Dive Assessment Tables.xlsx,” which is available for download from the NCHRP Research Report 934 webpage at www.trb.org.

Appendix C: Deep Dive Annotated Outline III-C-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. An example are population and employment forecasts, which are commonly identified as a major source of traffic forecasting 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 assumptions documented by the project team. In this example, population and employment forecasts are both an exogenous forecast and a project assumption. Past forecasting research has identified several exogenous forecasts and project assumptions as common sources of forecast error, including: • Macro-economic 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 the year the forecast was produced and the project’s opening year. The following table lists all exogenous forecasts and project assumptions for which observed data is available. It also includes an assessment of the accuracy of each item. <See Table III-C-2; note where actual data is not available.> <Assess overall accuracy of the sources of forecast error.> <Identify any model deficiencies/issues, data deficiency/issues and unexpected non- transportation changes that might have contributed to forecast error. If none, state accordingly.>

Table III-C-2. Input accuracy assessment table (columns in yellow require input). Items Definition Quantifiable Important Factor? Estimated Opening-Year Values Observed Opening-Year Values Difference (Est. - Obs.) % Difference Related Comments from the Report Data Sources Employment The actual employment (or GDP) differs from what was projected. Yes Yes/No - #DIV/0! Estimated Value: {Report Name/Website link/Model Run Details} Observed Value: Population/Household The actual population or households differ from what was projected. Yes - #DIV/0! Estimated Value: Observed Value: Car Ownership Actual car ownership differs from projection. Should note whether car ownership is endogenous or exogenous to the forecast. Yes - #DIV/0! Estimated Value: Observed Value: Fuel Price/Efficiency The average fuel price or fuel efficiency different from expectations. Yes - #DIV/0! Estimated Value: Observed Value: Travel Time/Speed Travel time comparison of the facility itself and alternative routes. Yes - #DIV/0! Estimated Value: Observed Value: Toll Sensitivity/Value of Time The sensitivity to tolls, or the value of the tolls themselves is in error. For example, Anam, S. (2016) study on Coleman Bridge found that the project considered two toll amounts ($1 and $0.75), however by the time of opening/horizon year it got to $0.85 and $2. Yes - #DIV/0! Estimated Value: Observed Value: Macro-economic Conditions Any effect of number of economic recession on forecast accuracy. No - #DIV/0! Estimated Value: Observed Value: Significant Land Use Changes Refers to changes in build environment that are not specific to the project (Andersson et al.(2016)). Flyvbjerg et al. (2006) found that 26% of projects experience problems regarding the change in land use. No - #DIV/0! Estimated Value: Observed Value: Changes in Technology Autonomous Vehicles, Automated Tolls. No - #DIV/0! Estimated Value: Observed Value: Study-Forecast Duration Number of years between forecast year and base year. According to Anam, S. et al. (2016) as the difference decreases, accuracy increases. Yes - #DIV/0! Estimated Value: Observed Value: Trip Generation/Travel Characteristics The availability of appropriate data and their quality, in particular traffic counts, network characteristics, travel costs, etc. No - #DIV/0! Estimated Value: Observed Value: Project Scope The project was built to different specifications than was assumed at the time of the forecast. For example, budget constraints meant that only 4 lanes were built instead of 6. Yes - #DIV/0! Estimated Value: Observed Value: Rest of Network Assumptions There were assumptions about related projects that would be constructed that differed from what was actually built. Yes - #DIV/0! Estimated Value: Observed Value: Model Deficiency/Issues Limitations of the model itself. This could include possible errors, or limitations of the method. For example, the project was built in a tourist area, but the model was not able to account for tourism. No - #DIV/0! Estimated Value: Observed Value: Data Deficiency/Issues Limitations of the data available at the time of the forecast. For example, erroneous or outdated counts were used as the basis for pivoting. No - #DIV/0! Estimated Value: Observed Value: Unexpected Changes In the latter portion of the 20th century, this could include the rise of 2-worker households or other broad social trends. In the 21st century, this could include technology changes, such as self-driving cars. No - #DIV/0! Estimated Value: Observed Value: Other Other issues that are not articulated above. No - #DIV/0! Estimated Value: Observed Value: Note: Interactive versions of Tables III-C-1 through III-C-4 can be accessed in the Excel file titled “Deep Dive Assessment Tables.xlsx,” which is available for download from the NCHRP Research Report 934 webpage at www.trb.org.

Appendix C: Deep Dive Annotated Outline III-C-5 5 Contributing Sources to Forecast Error Building upon the items discussed in Section 4, this section attempts to identify items 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. Adjusted forecasts for the critical roadways are computed by applying an elasticity to the relative change between the actual and predicted values for each item in Section 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 by exponentiating an elasticity of the common source errors and 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 + ) ∗ The results of this process are shown in the following table. Discuss insights and findings. <See Table III-C-3, which is sorted largest-to-smallest by the “remaining percent difference from forecast” column.>

Table III-C-3. Forecast adjustment table (elasticity adjustments). Seg# Items Definition Is Error Forecast Value Actual Value Change in Forecast Value Elasticity Effect on Forecast Actual Forecast Volume Adj Forecast Volume Remaining % Error for Adj Forecast Data Sources for Elasticity Comments 1 Employment The actual employment (or GDP) differs from what was projected. Yes/No - - 0% 0.30 0% - - #DIV/0! 1 Population/Household The actual population or households differ from what was projected. - - 0% 0.75 0% - - #DIV/0! 1 Car Ownership Actual car ownership differs from projection. Should note whether car ownership is endogenous or exogenous to the forecast. - - 0% 0% - - #DIV/0! 1 Fuel Price/Efficiency The average fuel price or fuel efficiency different from expectations. - - 0% (0.20) 0% - - #DIV/0! 1 Travel Time/Speed Travel time comparison of the facility itself and alternative routes. - - 0% 0% - - #DIV/0! 1 Toll Sensitivity/Value of Time The sensitivity to tolls, or the value of the tolls themselves is in error. For example, Anam, S. (2016) study on Coleman Bridge found that the project considered two toll amounts ($1 and $0.75), however by the time of opening/horizon year it got to $0.85 and $2. - - 0% 0% - - #DIV/0! 1 Study-Forecast Duration Number of years between forecast year and base year. According to Anam, S. et al. (2016) as the difference decreases, accuracy increases. - - 0% 0% - - #DIV/0! 1 Project Scope The project was built to different specifications than was assumed at the time of the forecast. For example, budget constraints meant that only 4 lanes were built instead of 6. - - 0% 0% - - #DIV/0! 1 Rest of Network Assumptions There were assumptions about related projects that would be constructed that differed from what was actually built. - - 0% 0% - - #DIV/0! 1 Original Traffic Forecast Original Forecasted Volume for Segment 1 0 0 0% N/A N/A 1 Adjusted Traffic Forecast Adjusted Volume for Segment 1 N/A N/A N/A - - #DIV/0! 2 Employment The actual employment (or GDP) differs from what was projected. Yes/No - - 0% 0.30 0% - - #DIV/0! 2 Population/Household The actual population or households differ from what was projected. - - 0% 0.75 0% - - #DIV/0! 2 Car Ownership Actual car ownership differs from projection. Should note whether car ownership is endogenous or exogenous to the forecast. - - 0% 0% - - #DIV/0! 2 Fuel Price/Efficiency The average fuel price or fuel efficiency different from expectations. - - 0% (0.20) 0% - - #DIV/0! 2 Travel Time/Speed Travel time comparison of the facility itself and alternative routes. - - 0% 0% - - #DIV/0! 2 Toll Sensitivity/Value of Time The sensitivity to tolls, or the value of the tolls themselves is in error. For example, Anam, S. (2016) study on Coleman Bridge found that the project considered two toll amounts ($1 and $0.75), however by the time of opening/horizon year it got to $0.85 and $2. - - 0% 0% - - #DIV/0! 2 Study-Forecast Duration Number of years between forecast year and base year. According to Anam, S. et al. (2016) as the difference decreases, accuracy increases. - - 0% 0% - - #DIV/0! 2 Project Scope The project was built to different specifications than was assumed at the time of the forecast. For example, budget constraints meant that only 4 lanes were built instead of 6. - - 0% 0% - - #DIV/0! 2 Rest of Network Assumptions There were assumptions about related projects that would be constructed that differed from what was actually built. - - 0% 0% - - #DIV/0! 2 Original Traffic Forecast Original Forecasted Volume for Segment 2 0 0 0% N/A N/A 2 Adjusted Traffic Forecast Adjusted Volume for Segment 2 N/A N/A N/A - - #DIV/0! <Repeat for all segments, and provide summary adjustments at the final rows of the table.> Note: Interactive versions of Tables III-C-1 through III-C-4 can be accessed in the Excel file titled “Deep Dive Assessment Tables.xlsx,” which is available for download at the NCHRP Research Report 934 webpage at www.trb.org.

Appendix C: Deep Dive Annotated Outline III-C-7 If the travel model or other method used to produce the traffic forecasts is available, then rerun the model or method, following the original method to the extent possible but using corrected exogenous forecasts and project assumptions. Report the results here. If the results are dramatically different from the elasticity-based approach, note this and rerun the model or method, altering the biggest contributors of forecast error individually. Note the “elasticities” and “cross-elasticities” from this process. <See Table III-C-4.>

Table III-C-4. Forecast adjustment table (travel model adjustments). <Repeat for all segments, provide summary adjustments at the final rows of the table.> Seg# Items Changes Made in the Model Is Error Old Model Value New Model Value Old Model Volume New Model Volume Observed Volume Difference (New - Obs.) % Difference from Observed Volume Comments/ Conclusions 1 Employment Yes/No - - - - - #DIV/0! 1 Population/Household - - - - - #DIV/0! 1 Car Ownership - - - - - #DIV/0! 1 Fuel Price/Efficiency - - - - - #DIV/0! 1 Travel Time/Speed - - - - - #DIV/0! 1 Toll Sensitivity/Value of Time - - - - - #DIV/0! 1 Study-Forecast Duration - - - - - #DIV/0! 1 Project Scope - - - - - #DIV/0! 1 Rest of Network Assumptions - - - - - #DIV/0! 2 Employment Yes/No - - - - - #DIV/0! 2 Population/Household - - - - - #DIV/0! 2 Car Ownership - - - - - #DIV/0! 2 Fuel Price/Efficiency - - - - - #DIV/0! 2 Travel Time/Speed - - - - - #DIV/0! 2 Toll Sensitivity/Value of Time - - - - - #DIV/0! 2 Study-Forecast Duration - - - - - #DIV/0! 2 Project Scope - - - - - #DIV/0! 2 Rest of Network Assumptions - - - - - #DIV/0! 1 - - - #DIV/0! 2 - - - #DIV/0! 3 - - - #DIV/0! 4 - - - #DIV/0! 5 - - - #DIV/0! 6 - - - #DIV/0! 7 - - - #DIV/0! 8 - - - #DIV/0! All Adjustments All the above items (wherever possible) are adjusted to match the actual values Note: Interactive versions of Tables III-C-1 through III-C-4 can be accessed in the Excel file titled “Deep Dive Assessment Tables.xlsx,” which is available for download at the NCHRP Research Report 934 webpage at www.trb.org.

Appendix C: Deep Dive Annotated Outline III-C-9 6 Discussion This section discusses how the findings in Section 5 relate to Section 3 and Section 4. This section should then address the following questions: Would the project decision have changed if the forecast accuracy or reliability were improved? How useful were the forecasts (what was their utility) in terms of providing the necessary information to the planning process? communicated? 7 Suggested Changes This section suggests improvements to the: Forecasting method, Forecasting practices, and/or Validation practices to be used for future projects. Supporting evidence from Sections 3–6 should be explicitly referenced. 8 Data Sources and References List and number in alphabetical order the data sources and references used to develop this report. Was risk and uncertainty considered in the forecast? How was it considered? How was it

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Accurate traffic forecasts for highway planning and design help ensure that public dollars are spent wisely. Forecasts inform discussions about whether, when, how, and where to invest public resources to manage traffic flow, widen and remodel existing facilities, and where to locate, align, and how to size new ones.

The TRB National Cooperative Highway Research Program's NCHRP Report 934: Traffic Forecasting Accuracy Assessment Research seeks to develop a process and methods by which to analyze and improve the accuracy, reliability, and utility of project-level traffic forecasts.

The report also includes tools for engineers and planners who are involved in generating traffic forecasts, including: Quantile Regression Models, a Traffic Accuracy Assessment, a Forecast Archive Annotated Outline, a Deep Dive Annotated Outline, and Deep Dive Assessment Tables,

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