Grants and Contracts Details
Accurate traffic forecasts for highway design are crucial for ensuring that public dollars are spent wisely; however, unlike in other countries, there is only a small set of empirical studies that have examined non-tolled traffic forecasting accuracy in the United States. These studies are important as they provide three critically important benefits: providing insight on observed inaccuracy levels to decision makers and the public; demonstrating the value of advanced models and data techniques; and identifying methods to improve traffic forecasting practice. Such studies are rare because of numerous challenges, including data availability, staff turnover, and absence of forecast preservation practice. These challenges are slowly being overcome in recent years as the importance of empirical accuracy reporting has grown. The need for the demonstrated value of advanced modeling and data techniques has also grown, as these techniques require significant resources. Other fields have demonstrated the effectiveness of such reviews, most notably the National Oceanic and Atmospheric Administration (NOAA) through their highly successful Hurricane Forecasting Improvement Program. In traffic forecasting, Wisconsin, Minnesota and Ohio have conducted targeted reviews of some traffic forecasts within in the past 6 years. The objective of this study is to analyze traffic forecasting accuracy using project traffic information from various state DOTs where records are available, report the findings, develop a recommended records retention policy, and suggest ways the traffic forecasting community could improve forecasting accuracy and communicate forecasts to the users. The research team will work with several state DOTs to create a combined database of project traffic forecasts, project characteristics and assumptions, exogenous forecasts, and actual traffic volumes. It is recognized that travel models are not used exclusively for traffic forecasts, especially for smaller projects. Linear regression based on historical traffic counts is commonly used in these instances. Therefore, the research team will need to account for the method used to produce the forecast (regression model, persistence model, travel model of record, refined travel model, etc.). Similar studies for toll roads and transit capital projects have noted the difficulty of gathering detailed project information post-construction. The research team will attempt to recreate the inputs as best as practically possible for a select number of projects. One key result of this study will be a recommended records retention policy so that similar analyses can be more easily performed in the future. The research team will then perform a rigorous analysis to accomplish the following: 1. Develop metrics and processes for evaluating traffic forecasts 2. Evaluate traffic forecast accuracy across several dimensions: a. Functional classes b. Area types c. Volume groups d. Size of metropolitan areas e. Project sizes f. Types of projects 3. Determine under what conditions forecasting accuracy improves when travel models are used to develop the forecast. 4. Enumerate contributors to forecast inaccuracy and suggest methods to mitigate each. 5. Evaluate methods for improving communication of forecast uncertainties. 6. Provide recommendations on instituting an ongoing review of forecast accuracy, including a recommended records retention policy. This study will fill a major gap in the United States traffic forecasting industry by providing insights on observed inaccuracy levels and identifying problems with non-tolled traffic forecast practice. Similar studies for toll roads and transit capital projects have noted the difficulty of gathering detailed project information post-construction. One key result of this study will be a recommended records retention policy so that similar analyses can be more easily performed in the future. The results of this study might also demonstrate the value of the implementation and application of modeling techniques, as opposed to regression or persistence models. Providing empirical evidence, should it exist, of the value of these techniques would significantly increase the support for investments in these areas. This study is also intended to serve as a prototype analysis that can be duplicated by all state DOTs.
|Effective start/end date||2/14/17 → 11/22/19|
- National Academy of Sciences: $349,932.00
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