Implementation of Traffic Data Quality Verification for Weigh-in-Motion Sites

Principal Investigator(s):

Chen-Fu Liao, Former Researcher, Mechanical Engineering


  • Gary Davis, Professor, Civil, Environmental and Geo-Engineering

Project summary:

Weigh-In-Motion (WIM) system tends to go out of calibration from time to time, and as a result they generate biased and inaccurate measurements. Several external factors such as vehicle speed, weather, pavement conditions, etc. can be attributed to such anomalies. To overcome this problem, a statistical quality control technique is warranted that would provide the WIM operator with some guidelines whenever the system goes out of calibration. A mixture modeling technique using Expectation Maximization (EM) algorithm was implemented to divide the Gross Vehicle Weight (GVW) measurements of vehicle class 9 into three components (unloaded, partially loaded, and fully loaded). Cumulative Sum (CUSUM) statistical process technique was used to identify any abrupt change in mean level of GVW measurements. Special attention was given to the presence of auto-correlation in the data by fitting an auto-regressive time series model and then performing CUSUM analysis on the fitted residuals. A data analysis software tool was developed to perform EM Fitting and CUSUM analyses. The EM analysis takes monthly WIM raw data and estimates the mean and deviations of GVW of class 9 fully loaded trucks. Results of the EM analyses are stored in a file directory for CUSUM analysis. Output from the CUSUM analysis will indicate whether there is any sensor drift during the analysis period. Results from the analysis suggest that the proposed methodology is able to estimate a shift in the WIM sensor accurately and also indicate the time point when the WIM system went out of calibration. A data analysis software tool, WIM Data Analyst, was developed using the Microsoft Visual Studio software development package based on the Microsoft Windows .NET framework. An open source software tool called R.NET was integrated into the Microsoft .NET framework to interface with the R software, which is another open source software package for statistical computing and analysis.

Project details:

  • Project number: 2014027
  • Start date: 10/2013
  • Project status: Completed
  • Research area: Transportation Safety and Traffic Flow
  • Topics: Data and modeling

Reports or Products: