"A question often asked in transportation seminars and workshops is how do I validate short-term counts with so little data collected? This project addresses one aspect of that validation process. Exciting and unique not only because of its functionality, it breaks ground on several new fronts as well. First, the analysis will provide a workable technique to validate short-term counts and function as one component within a quality control process. Statistically, a large dataset for a spatial analysis is 250 points. We are attempting to model 37,000 points (permanent count stations). The size and complexity of the data itself poses problems that can't be addressed through standard ""packaged"" analysis software. This project is a "cutting edge" prototype that can be modified to fit other DOT's validation processes. We are pleased to be participating in this exciting research."
-Susan Cosper
Within North Carolina, approximately 35,000 traffic counts are recorded annually for the purpose of monitoring the flow of traffic. These counts play a crucial role in allocation of resources for the maintenance, upgrade, and expansion of traffic infrastructure. The need for reliable, edited, and validated traffic count data is well acknowledged by the Federal Highway Administration (FHWA) and the American Association of State Highway and Transportation Officials (AASHTO).
This research addresses this need by developing a statistically defensible approach to achieving spatial continuity of traffic counts as part of the editing and validation process. A letter of support from the Director of the Office of Highway Policy Information of FHWA in favor of the research gives further credence to the project.
At present, the practice within NCDOT for editing and validation count data is to manually and visually compare current counts to counts from previous years and neighboring stations. If a count is considered unusual, it is often modified to make it more similar to neighboring counts. This process is very slow, is prone to individual subjectivity and bias, encourages excessive adjustments to counts, and does not conform to the FHWA and AASHTO recommendations for incorporating spatial analysis.
This research will significantly improve the process by increasing the accuracy of reported counts, by reducing the time delay between data collection and reporting, and by providing easily customized reports of traffic counts to NCDOT departments and customers.
