Traffic Forecasting Accuracy Assessment Research (2020) / Chapter Skim
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III-G-1 A P P E N D I X G Large-N Analysis Contents III-G-2 1 Introduction III-G-3 2 Available Data and Key Challenges III-G-3 2.1 Data III-G-5 2.2 Database Structure III-G-7 2.3 Decision Variables III-G-9 3 Method III-G-9 3.1 Methods Used in Existing Literature III-G-10 3.2 Evaluation Year III-G-11 3.3 Definition of Errors III-G-12 3.4 Distribution of Errors III-G-13 3.5 Bias Detection III-G-14 3.6 Level of Analysis: By Segment or by Project III-G-15 3.7 Data Cleaning and Filtering III-G-16 3.8 Outliers III-G-16 3.9 Calculating the Number of Lanes Required III-G-18 4 Data Exploration III-G-20 4.1 Overall Distribution III-G-22 4.2 Forecast Volume III-G-24 4.3 Functional Class III-G-25 4.4 Area Type III-G-26 4.5 Type of Project III-G-27 4.6 Tolls III-G-28 4.7 Year Forecast Produced III-G-30 4.8 Opening Year III-G-32 4.9 Forecast Horizon III-G-33 4.10 Unemployment Rate in Opening Year III-G-34 4.11 Change in Unemployment Rate III-G-35 4.12 Forecast Method III-G-36 4.13 Type of Forecaster III-G-37 4.14 Effect on Number of Lanes III-G-38 5 Econometric Analysis III-G-39 5.1 Base Model III-G-41 5.2 Inclusive Model for Inference III-G-44 5.3 Forecasting Model III-G-48 References
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III-G-2 Traffic Forecasting Accuracy Assessment Research 1 Introduction To conduct the Large-N analysis, the NCHRP 08-110 project team collected data from numerous sources and created a database (hereafter called the "forecast accuracy database")
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Appendix G: Large-N Analysis III-G-3 publicly -- analyzed 104 projects. The Standard & Poor's report found that the demand forecasts for those projects were optimistically biased, and that this bias persisted into the first 5 years of operation.
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III-G-4 Traffic Forecasting Accuracy Assessment Research information. For example, data from Florida D4 and D5 were provided in different formats -- D4 data were provided in Excel format while D5 data were extracted from scanned PDF reports.
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Appendix G: Large-N Analysis III-G-5 appendix)
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III-G-6 Traffic Forecasting Accuracy Assessment Research how the final design was selected.) The primary fields in this initial forecast database were classified into three types: 1.
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Appendix G: Large-N Analysis III-G-7 2.3 Decision Variables Based on the nature of the forecast accuracy database, we could select some variables that might dictate future adjustments in the forecasts. These variables are: the type of project, the method used, roadway type, area type, and the forecast horizon (the difference between the year the forecast was produced and the year of opening)
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III-G-8 Traffic Forecasting Accuracy Assessment Research urban area as a territory that has at least 2,500 people. The percentage of people living in rural areas in a county determines whether the county is rural (100%)
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Appendix G: Large-N Analysis III-G-9 Table III-G-7. Descriptions of forecasting agency in the forecast accuracy database.
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III-G-10 Traffic Forecasting Accuracy Assessment Research Table III-G-8. Summary of existing Large-N methodologies.
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Appendix G: Large-N Analysis III-G-11 3.3 Definition of Errors One of the differences in methodologies in previous Large-N studies has been how they have defined the Predicted Volume minus the Actual Volume such that a positive result is an overprediction. Odeck and Welde (2017)
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III-G-12 Traffic Forecasting Accuracy Assessment Research Source: Flyvbjerg, Holm, and Buhl (2006) Source: Bain (2009)
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Appendix G: Large-N Analysis III-G-13 3.5 Bias Detection Odeck and Welde (2017) employed an econometric approach to determine the bias and the efficiency of the estimates by regressing the forecast value to actual value using the following equation: = + ̂ + , where = the actual traffic on project i, ̂ = the forecast traffic on project i, = a random error term, and α and β = estimated terms in the regression.
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III-G-14 Traffic Forecasting Accuracy Assessment Research of a long delay. It has also been used to estimate error bounds for real-time traffic predictions (Pereira et al.
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Appendix G: Large-N Analysis III-G-15 We report the distribution of percent difference from forecast both at a project level and a segment level. The results, presented later in this chapter, show that averaging to the project level appears to average out some of the errors observed at a segment level.
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III-G-16 Traffic Forecasting Accuracy Assessment Research be the same as the most prevalent one among the segments. For example, if most of the segments in a project were of "Improvement Type 1," the project was considered to be of Improvement Type 1.
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Appendix G: Large-N Analysis III-G-17 or project may require. Using the best available current-year data, and projecting future values of directional design hourly volume (DDHV)
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III-G-18 Traffic Forecasting Accuracy Assessment Research The HCM-recommended range of values for selecting appropriate 30and 30factors for project forecast is also given in the following figures. Figure III-G-3.
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Appendix G: Large-N Analysis III-G-19 3. Actual traffic volumes to compare against the forecast volumes are taken for the year after the project has been completed (and for records in the database that did not have a project completion date, a buffer of at least 1 year was created based on the type of project)
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III-G-20 Traffic Forecasting Accuracy Assessment Research Table III-G-11. Descriptive variables for analysis.
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Appendix G: Large-N Analysis III-G-21 Figure III-G-5. Distribution of percent difference from forecast (segment level)
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III-G-22 Traffic Forecasting Accuracy Assessment Research We should expect overpredictions because, in many cases, these forecasts are used in design engineering. A design based on overpredicted traffic will typically be overbuilt and will not see that extra capacity utilized.
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Appendix G: Large-N Analysis III-G-23 Figure III-G-8. Percent difference from forecast as a function of forecast volume (project level)
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III-G-24 Traffic Forecasting Accuracy Assessment Research Traffic Forecast Range (ADT) Observations Mean Absolute Percent Difference from Forecast Mean Median Standard Deviation 5th Percentile 95th Percentile 0–3000 133 24.59 -1.85 -5.75 42.15 -45.01 75.17 3001–6000 142 20.53 -0.37 -4.64 29.74 -36.50 50.33 6001–9000 125 16.75 -5.68 -8.80 21.94 -35.29 36.67 9001–13000 145 15.59 -4.66 -7.29 19.99 -31.34 34.45 13001–17000 143 17.41 -6.20 -6.53 21.61 -37.76 30.65 17001–22000 113 17.98 -5.65 -8.31 25.47 -41.62 37.85 22001–30000 133 19.54 -5.65 -8.47 25.36 -40.31 41.75 30001–40000 115 15.56 -9.78 -10.26 18.23 -39.54 12.26 40001–60000 137 13.18 -8.95 -7.68 16.01 -34.44 7.49 60000+ 105 10.20 -8.96 -7.90 9.90 -24.50 3.68 One observation from Table III-G-14 is that as the forecast volume increases, the distribution of the percent difference from forecast has smaller spreads in addition to the MAPDFF value getting smaller.
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Appendix G: Large-N Analysis III-G-25 Figure III-G-9. Distributions of percent difference from forecast by functional class (segment-level analysis)
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III-G-26 Traffic Forecasting Accuracy Assessment Research Table III-G-16 addresses forecast inaccuracy by area type at the segment level. As seen in the table, forecasts for both rural and urban areas are mostly overpredicting (i.e., actual traffic is less than forecast, as shown by 65% of the links in rural areas and 72% of links in urban areas)
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Appendix G: Large-N Analysis III-G-27 Among the 1,291 projects, the forecast accuracy database contained forecast and actual count information on only 28 new construction projects, whereas projects on existing roadways numbered 788. About 75% of the projects on existing roadways in the database had percentage differences below 0% (i.e., overpredicted the traffic)
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III-G-28 Traffic Forecasting Accuracy Assessment Research Figure III-G-12. Distribution of percent difference from forecast by toll types (segment-level analysis)
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Appendix G: Large-N Analysis III-G-29 Figure III-G-13. Distribution of percent difference from forecast by the year forecast produced.
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III-G-30 Traffic Forecasting Accuracy Assessment Research Table III-G-20. Forecast inaccuracy for projects on existing roadways, by year forecast produced.
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Appendix G: Large-N Analysis III-G-31 Table III-G-21. Forecast inaccuracy by project opening year.
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III-G-32 Traffic Forecasting Accuracy Assessment Research Table III-G-22. Forecast inaccuracy in projects on existing roadways, by opening year.
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Appendix G: Large-N Analysis III-G-33 Forecasts that go beyond 5 years tend to be less accurate (although still relatively unbiased) and when the project opens the actual traffic count tends to have a higher percent difference from forecast (90% of the data points fall within -45% to 72%, with a MAPDFF of 29%.)
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III-G-34 Traffic Forecasting Accuracy Assessment Research Of the projects that opened in years with an unemployment rate in the range of 7–8%, 72% of the forecasts overpredicted the traffic, with average absolute deviation of 17.3%. Comparing between the ranges, unemployment rates between 3 and 5 seem to produce the maximum absolute deviation from forecast volume.
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Appendix G: Large-N Analysis III-G-35 least 2 points. Some 90% of the projects fall either between -36.1% to 26.67% for change of 2–4% and between -35.26% to 18.78% for change of 4–6%.
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III-G-36 Traffic Forecasting Accuracy Assessment Research Figure III-G-20. Distributions of percent difference from forecast by forecast method.
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Appendix G: Large-N Analysis III-G-37 the projects lie between -35.83% and 31.42%) , as is the mean absolute deviation (MAPDFF of 17.36% compared to 21.47% for state DOT-produced forecasts)
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III-G-38 Traffic Forecasting Accuracy Assessment Research 5 Econometric Analysis The uncertainties involved in forecasting traffic call for assessing the risks and subsequently developing a range of traffic forecasts that can be expected on a project. Considering the current dataset to be representative (i.e., "national average")
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Appendix G: Large-N Analysis III-G-39 5.1 Base Model In the first model, we regressed the actual count on the forecast traffic volume. The structure follows Equation III-G-3 (reported previously)
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III-G-40 Traffic Forecasting Accuracy Assessment Research that for 5% of the forecasts we do, the actual traffic will be less than 5,415, and that for 5% of forecasts we do, the actual traffic will be more than 17,153 ADT. The 20th and 80th percentile values can be calculated similarly.
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Appendix G: Large-N Analysis III-G-41 Figure III-G-22. Expected ranges of actual traffic (base model)
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III-G-42 Traffic Forecasting Accuracy Assessment Research 1 has a value of -0.1 then it means that the median actual value would be 10% lower than the forecast. If 1 has a value of +0.1 then it means that the median actual value would be 10% higher than the forecast.
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Appendix G: Large-N Analysis III-G-43 5th Percentile 20th Percentile 50th Percentile 80th Percentile 95th Percentile Pseudo R-Squared 0.513 0.662 0.762 0.827 0.853 Coef. t value Coef.
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III-G-44 Traffic Forecasting Accuracy Assessment Research Figure III-G-23. Comparison of actual traffic for arterials and Interstate for 80th percentile using inclusive model.
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Appendix G: Large-N Analysis III-G-45 4. Forecast horizon, or how many years into the future traffic is being forecast, 5.
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III-G-46 Traffic Forecasting Accuracy Assessment Research Table III-G-34. Contributions of specific values to the equation.
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Appendix G: Large-N Analysis III-G-47 Table III-G-36. Percent difference from forecast window for forecast model on specified values.
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III-G-48 Traffic Forecasting Accuracy Assessment Research References Australian Government (2012)
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Appendix G: Large-N Analysis III-G-49 Nicolaisen, M
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