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. The significant amount of data being collected on a daily basis by WIM system requires a substantial amount of effort to verify data accuracy and ensure data quality. However, the WIM system itself presents difficulty in obtaining accurate data due to sensor characteristics that are sensitive to vehicle speed, weather condition, and changes in surrounding pavement conditions. This research focuses on developing a systematic methodology to detect WIM sensor bias and support WIM calibration in a more efficient manner. An implementation guideline for WIM sensor calibration was developed. 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. A popular statistical process control technique, Cumulative Sum (CUSUM) was performed on daily mean GVW estimates for fully loaded class 9 vehicles to identify and estimate any shift in the WIM sensor. Special attention was given when presence of auto-correlation in the data was detected by fitting time series model and then performing CUSUM analysis on the fitted residuals. Results from the analysis suggested that the proposed methodology was able to estimate shift in the WIM sensor accurately and also indicated the time point when the system went out-of-calibration. An out-of control CUSUM behavior is solely attributed to a plausible shift in WIM sensor. However, several case studies indicated that this might not be true always. The proposed methodology first identified a learning period. The learning sample was then analyzed to fit a time series model. To identify if there is any shift in WIM sensor, a CUSUM analysis on residuals, which were obtained from predictions, on testing sample was performed. The underlying assumption of the methodology is if the data is generated from a stable process then the predictions based on the model estimated from the learning sample should consistently capture the variation in the testing sample. Any introduction of instability or sensor shift in the testing sample should be reflected in the residuals. Then CUSUM algorithm was implemented to detect such shift in WIM sensor. This methodology could benefit state agencies such as MnDOT by identifying when calibration was lost and subsequently a proper modification factor could be applied to the out-of-calibration data to adjust for the bias. Additional unknown factors besides WIM sensors, such as varying truck population and other external factors, are found to influence WIM measurements. With only limited information available, it is not possible to identify such factors and provide explanations for such an inconsistent pattern. At this point the goal is to propose a methodology that would alert the WIM operator whenever such anomaly is detected. To identify such scenarios a revised implementation plan is proposed and tested for a simulated set of observations. Although, the proposed plan looks promising, further investigation and analysis on historical data will be performed for validation and final implementation. 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 (https://rdotnet.codeplex.com/) was integrated into the Microsoft .NET framework to interface with the R software (http://www.r-project.org/), which is another open source software package for statistical computing and analysis. The developed WIM data analyst tool consists of two key components, i.e., EM Fitting and CUSUM analyses. In addition, a HTML online help document was also created and embedded into the software tool to provide comprehensive online help information. The EM analysis takes a monthly WIM raw data (CSV) file of each WIM station from MnDOT 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. The CUSUM analysis takes inputs from the EM results and a calibration file based on MnDOT calibration logs to model a learning sample and estimates the residuals between the prediction and WIM observation. Output from the CUSUM analysis will indicate whether there is any sensor drift during the analysis period. (Author/publisher)
Samenvatting