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A Statistical Process Control Approach for Traffic Data Quality Verification and Sensor Calibration for Weigh-in-Motion Systems

Weigh-In-Motion (WIM) systems have been widely used by state agencies to collect the traffic data on major state roadways and bridges to support traffic load forecasting, pavement design and analysis, infrastructure investment decision making, and transportation planning. However, WIM system itself poses difficulties in obtaining accurate data due to sensor characteristics which can be sensitive to vehicle speed, weather condition, and changes in surrounding pavement conditions. This study focuses on developing a systematic methodology to detect WIM sensor bias and enhance current practices for WIM calibration. A mixture modeling technique using Expectation Maximization (EM) algorithm was developed to divide the vehicle class 9 Gross Vehicle Weight (GVW) into three normally distributed components, unloaded, partially loaded, and fully loaded trucks. Then well-known Statistical Process Control (SPC) technique, CUSUM was applied to EM estimates of daily mean GVW for fully loaded trucks to identify and estimate shifts in the WIM sensor. 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. 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 system went out-of-calibration. This methodology can be effectively implemented by state agencies resulting in a more accurate and reliable WIM data.