The National Academies

NCHRP 20-50(20) [Anticipated]

LTPP Data Analysis: Develop Tools to Improve Accuracy of Traffic Loading Data Collection

  Project Data
Source: Federal Highway Administration
Funds: $350,000
Staff Responsibility: Camille Crichton-Sumners
Fiscal Year: 2018

This project has been tentatively selected and a project statement (request for proposals) is expected in July 2017. The project statement will be available on this world wide web site. The problem statement below will be the starting point for a panel of experts to develop the project statement.

There is a need in the highway community to know how accurate and consistent weigh-in-motion (WIM) systems are for collecting traffic loading data. This data is a critical input in pavement and bridge designs and is used for making transportation and freight planning decisions, and in highway safety investigations. There are many potential sources of error in WIM measurements. Currently, it is nearly impossible for the traffic data collectors to evaluate the relative influence of each source. This leads to diminished WIM system performance and lower quality WIM data.

For over a decade, the LTPP program has collected a massive amount of consistent, high-quality WIM data, along with information about the performance of WIM equipment, including WIM validation and calibration data from 24 LTPP SPS test sites across the United States. This unique data set provides the opportunity to develop more advanced WIM tools to help state highway practitioners in pavement smoothness evaluation, WIM site selection, WIM sensor selection or replacement, WIM calibration procedures and frequency, and WIM data quality acceptance. These tools could help minimize WIM measurement errors related to temperature and seasonal effects, vehicle speed, changes in truck populations, data sampling frequencies, system age, and other factors related to WIM scale technology.  

The objective of this research is to develop the next generation of tools and procedures for characterizing and managing the variability and bias in WIM measurements through more effective site selection, WIM sensor technology considerations, effective WIM calibration, and quality control checks of the data.

Now is the time to examine the lessons learned and to improve/develop new tools for highway agencies to collect reliable WIM data. These tools will allow for more effective WIM data quality assessment and more efficient, data-driven WIM calibration scheduling. Better-quality WIM data will lead to more accurate and cost-effective pavement and bridge designs, more informed transportation and freight planning decisions, and more insightful highway safety investigations.

To create a link to this page, use this URL: http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=4412