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I-59 The research that produced this report has provided a starting point for a broader body of work on traffic forecast accuracy. Such assessments have always been limited by available data, largely because it is cumbersome to compile data on forecasts several years after they are made. Part I of this report has provided guidance on how to archive traffic forecasts and associated data with the goal of making the activity efficient so it will be done systematically. It also has provided guidance on how to use those data. The natural next step is to implement these recommendations. Appendix E (in Part III of this report) presents an implementation plan that outlines the steps to be taken to promote the implementation of these recommendations. Such implementation may itself involve further research. As discussed, the data used in this study were selected based on availability, and users may find greater value in analyzing projects specifically relevant to their situation; however, a practical and effective starting point may be further analysis using the data from this research (see Chapter 3). Because most of the projects in the current database are not yet open, the sample of projects available for analysis is expected to increase with each passing year. Agencies also may conduct analysis of data compiled from their own traffic forecasts, which may be more directly relevant to their interests. As the analysis of forecast accuracy continues, new insights will undoubtedly be gained. How- ever, such work need not be limited to the recommendations provided in this report. Ample room exists to expand the scope of the analysis conducted. Several possible directions are: ⢠In addition to a comparison of post-opening counts to the project forecast, also compare the pre-opening counts to the base year or no-build forecast. Such an assessment should focus on the change between the pre- and post-opening volumes. ⢠Measure forecast versus actual outcomes on screen lines to provide insight into whether any inaccuracy is attributable to a trip table prediction problem or an assignment problem. ⢠Evaluate the forecast versus actual outcomes not only on the project itself, but also in compar- ison to broader VMT trends throughout the region. This evaluation would provide additional insight into whether any inaccuracy is specific to the project or regional in nature. ⢠Consider forecast outcomes beyond ADT, such as peak period traffic volumes, roadway speeds, and truck volumes. ⢠Expand the research to include additional types of projects, such as transit projects, toll roads, and express lanes. ⢠Examine design-year forecasts in addition to opening-year forecasts. ⢠Consider side-by-side tests of competing methods to directly compare the accuracy of each. ⢠Compile the data necessary to test a broader range of methods. ⢠Further consider the implications for the Large-N analysis of missing data fields being cor- related with specific agencies providing the data. C H A P T E R 6 Implementation and Future Research
I-60 Traffic Forecasting Accuracy Assessment Research ⢠Test the importance of clustering effects in the data. The statistical methods used in this research project assume that each observation is independent; however, in fact, some may be correlated. This issue is most prominent in the segment-level analysis, where it is reasonable to expect each segment to be correlated with other segments from the same project. Good examples of the first four extensions of research appear in Highway Englandâs POPE of Major Schemes. For example, Figure I-11 provides a comparison of the after-minus-before forecast versus the after-minus-before counts, highlighting that this particular forecast does a reasonably good job of predicting the change in traffic. The POPE reports provide numerous examples of post-opening evaluations, not only of the accuracy of forecasts, but also of the effectiveness of the project against its stated goals. The POPE reports are available online at: https://www.gov.uk/government/collections/post-opening-project-evaluation-pope-of-major- schemes. Additional research along these lines will continue to inform practitionersâ understanding of the uncertainty around traffic forecasts, and will provide further opportunities to reduce that uncertainty as well as any bias present. Figure I-11. Example pre- and post-opening traffic forecast comparison (Atkins 2017).