Traffic Forecasting Accuracy Assessment Research (2020) / Chapter Skim
Currently Skimming:
Pages 15-36
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 15... ...
I-3 1.1 Purpose of the Guidance Document 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.
|
From page 16... ...
I-4 Traffic Forecasting Accuracy Assessment Research – Chapter 2 describes how to use measured forecast accuracy to communicate uncertainty around new forecasts. The person preparing a traffic forecast would take these steps to identify and communicate the uncertainty in terms of a range at the time the forecast is made.
|
From page 17... ...
Introduction I-5 sometimes are used interchangeably, but in scientific contexts, reliability has a distinct meaning. In a scientific context, reliability is the likelihood that someone repeating an experiment will obtain the same result.
|
From page 18... ...
I-6 Traffic Forecasting Accuracy Assessment Research to intentional distortion of forecasts. The project team's review of the literature revealed that the length of the forecast horizon, the nature of the facility for which forecasts are made, stability or volatility in population growth and economic factors, all influence forecast accuracy in predicable ways, and that forecasts will always be influenced by some factors that are unexpected, unpredictable, and therefore difficult to anticipate.
|
From page 19... ...
Introduction I-7 • Identified methods for improving flexibility and adaptability of available forecasting techniques to changing assumptions and input data; • Considered alternative ways of incorporating risk and uncertainty into forecasts; and • Identified potential methods that can help the traffic forecasting industry improve forecasting usefulness and accuracy while improving their ability to communicate and explain these forecasts to affected communities. A review of the literature revealed that most prior studies of traffic forecasting accuracy had adopted either of two complementary approaches to assess the accuracy of the forecasts.
|
From page 20... ...
I-8 Traffic Forecasting Accuracy Assessment Research Combining the Large-N analysis with the deep dives was intended to reveal as many sources of error in traffic forecasts as possible; however, it was understood that some sources of error would remain unexplained simply because it is impossible to account for every deviation between forecasts and measured traffic. This constraint results in part because the historical data drawn from numerous, dissimilar sources has been collected with varying levels of precision.
|
From page 21... ...
Introduction I-9 The forecast accuracy database contains unique project identification numbers and each project's improvement type, facility type, location, and length. For traffic forecasts, the database also includes information about who made the forecast, the year each forecast was made, the forecast target year, and whether the forecast was for the opening year, a middle year in the life of the project, or a distant future year as far as 20 years after opening.
|
From page 22... ...
I-10 Traffic Forecasting Accuracy Assessment Research error as the predicted volume minus the measured volume; using this metric, a positive result represents an overprediction. Other studies defined error as the measured volume minus the predicted volume; using this metric, a positive value represents an underprediction.
|
From page 23... ...
Introduction I-11 or segments, within the project boundary. These links can be on different alignments or can carry traffic in differing directions.
|
From page 24... ...
I-12 Traffic Forecasting Accuracy Assessment Research are not systematically higher or lower, but they could have larger errors as measured by the MAPE. To explore this issue, the project team extended the regression framework to use quantile regression instead of OLS regression.
|
From page 25... ...
Introduction I-13 the biases in the forecasts based on the variables described in the previous section. The models were developed for the 5th, 20th, 50th (median)
|
From page 26... ...
I-14 Traffic Forecasting Accuracy Assessment Research The project team could make a number of observations from the Large-N analysis. These observations and the supporting data are described in more detail in Part II but are summarized in these 11 statements: 1.
|
From page 27... ...
Introduction I-15 ability to conduct these deep dives was limited by time, money, and, most critically, by the availability of data. Six cases is a small number, and the documented information available for one of the six cases was insufficiently complete to allow confident conclusions; however, the insights gained were substantial and richly complemented what had been learned from the Large-N analysis.
|
From page 28... ...
I-16 Traffic Forecasting Accuracy Assessment Research that had been used to make the forecasts were available to the team. In these cases, the project team was able to rerun the models using the data for the population and employment growth that had actually occurred and the roadway features that had changed.
|
From page 29... ...
Introduction I-17 1.3.1 Lessons Learned One of the most important and overarching conclusions from this study is that agencies should take far more seriously the analysis of their past forecasting errors so that they can learn from the cumulative record. It is tempting to assert that the future is always uncertain and thus forecasts of the future will always be wrong, but doing that is far too glib.
|
From page 30... ...
I-18 Traffic Forecasting Accuracy Assessment Research typically not recorded as part of the data made available to the project team, and because model differences vary even among agencies that state they are using the same models. The best way to compare types of models would be to produce competing forecasts for the same set of projects and compare their accuracy.
|
From page 31... ...
Introduction I-19 To evaluate whether a forecast is sufficient to inform the decision at hand, the authors of this report recommend that forecasters explicitly acknowledge the uncertainty inherent in forecasting by reporting a range of forecasts. If an actual outcome at the low end or high end of the range would not change the decision, then the project's sponsors can safely proceed with little worry about the risk of the project.
|
From page 32... ...
I-20 Traffic Forecasting Accuracy Assessment Research to be planned and to open. In addition, these data are general, and the experience of individual forecasting agencies may vary.
|
From page 33... ...
Introduction I-21 Within the context of these three levels, Chapter 3 provides specific recommendations of what to archive and how to do so efficiently. Recommendation 3: Periodically report the accuracy of forecasts relative to observed data.
|
From page 34... ...
I-22 Traffic Forecasting Accuracy Assessment Research The project team recommends that as they set out to improve their traffic forecasting methods or to update their travel demand models, transportation agencies consider the results of past forecast accuracy assessments. This assessment may take several forms: • If deep dives have revealed specific sources of error in past forecasts, those sources should be given extra scrutiny in developing new methods.
|
From page 35... ...
Introduction I-23 Even if a forecaster is very objective and careful in the analysis, the forecast can turn out to be inaccurate. For example, an unexpected recession could occur at about the time a project opens.
|
From page 36... ...
I-24 Traffic Forecasting Accuracy Assessment Research their ISO 9000 standard (International Standards Organization n.d.)
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.