Traffic Forecasting Accuracy Assessment Research (2020) / Chapter Skim
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Pages 94-113
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From page 94... ...
II-20 Deep Dives 3.1 Introduction to the Deep Dives The Large-N analysis could be used to measure the error and could shed light on certain factors associated with forecast errors, but it did not explain why the forecasts might be in error. The project team used deep dives to fill that gap to the extent possible.
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Deep Dives II-21 happened to be good at keeping their own records or who had seen the value in saving the information. The project team looked for big projects with the idea that, if the projects were more important to start with, they would be better documented and would show more meaningful impacts.
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From page 96... ...
II-22 Traffic Forecasting Accuracy Assessment Research review simply identified that the topic could be a source of error from a logical standpoint, rather than clearly showing the amount of error due to that cause. Several general observations can be made about the sources of error as cited in the literature: • Significant overlap exists in the identified sources of error.
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Deep Dives II-23 The deep dives would provide readers with information about where they should focus their efforts to improve forecasts. 3.2.2 Procedure for Analysis The UK Post-Opening Project Evaluations (POPEs)
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From page 98... ...
II-24 Traffic Forecasting Accuracy Assessment Research A reference model was used as a comparison forecast to see how much better the actual forecasts did relative to pre-existing trends. The reference model was estimated as a time-series model regressing VKT on GDP/capita and fuel price: eVKT GDP cap fuel priceln ln ln , (II-8)
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Deep Dives II-25 adjustment building incrementally upon the others that preceded it. The analysis shows that correcting for these five assumptions would have reduced the root mean square error (RMSE)
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II-26 Traffic Forecasting Accuracy Assessment Research of a particular outcome occurring. The authors recommend using Monte Carlo simulations to generate possible scenarios; however, such an approach went beyond the analysis conducted in this project.
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Deep Dives II-27 Each deep dive followed a similar structure: working through the list of factors, attempting to identify whether each item was an important source of error for the forecast, and, if so, attempting to quantify how much it would have changed the forecast if the forecasters had gotten it right. The last column in Table II-13 also identifies whether the project team expected to be able to quantify the effect of that item on the resulting forecast.
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II-28 Traffic Forecasting Accuracy Assessment Research • Comparison to Actual Outcomes. Notes on the actual project outcomes and associated data sources are placed in this section.
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Deep Dives II-29 Source: Map data: Google Earth, annotated by NCHRP 08-110 project team W Elm St 5 6 8 9 10 11 1 2 3 4 7 These are the 11 segments identified for traffic volume accuracy assessment. Figure II-6.
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II-30 Traffic Forecasting Accuracy Assessment Research 3.3.2 Indian Street Bridge, Palm City, Florida The Indian Street Bridge is a new bridge construction project located in Palm City, Florida (Martin County)
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Source: Map data: Indian Street Bridge PD&E, Design Traffic Technical Memorandum, Florida DOT (January 23, 2003) Figure II-7.
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II-32 Traffic Forecasting Accuracy Assessment Research available on trips from Big Data sources (e.g., Streetlight or AirSage data) before and after the recession years.
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Deep Dives II-33 Overall, the prevailing macroeconomic conditions around the opening year played a major part in the accuracy of the forecasts for this project. Other exogenous factors causing the overestimate may be the increase in fuel prices and an increase in retirees.
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II-34 Traffic Forecasting Accuracy Assessment Research 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.
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From page 109... ...
Deep Dives II-35 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.
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II-36 Traffic Forecasting Accuracy Assessment Research to do the study)
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Deep Dives II-37 • Socioeconomic variables such as population and employment at sub-regional levels (focusing on the project corridors) ; • Regional VMT and vehicle-hours traveled (VHT)
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II-38 Traffic Forecasting Accuracy Assessment Research area concentrated on Segment 5, the 3.3-mile part of the roadway that was covered by the FEIS (i.e., the Memorial Drive to County M segment)
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Deep Dives II-39 The traffic forecasting accuracy improved after correcting the exogenous population forecast. However, the fuel price adjustment increased the PDFF.
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