Chatterjee, Indrajit

This presentation pertains to report 2015-18, Implementation of Traffic Data Quality Verification for WIM Sites.

This project explores the possibility of using high-resolution traffic signal data to evaluate intersection safety. Traditional methods using historical crash data collected from infrequently and randomly occurring vehicle collisions can require several years to identify potentially risky situations. By contrast; the proposed method estimates potential traffic conflicts using high-resolution traffic signal data collected from the SMART-Signal system. The potential conflicts estimated in this research include both red-light running events; when stop-bar detectors are available; and crossing (i.e. right-angle) conflicts. Preliminary testing based on limited data showed that estimated conflict frequencies were better than AADT for predicting frequencies of angle crashes. With additional validation this could provide a low-cost and easy-to-use tool for traffic engineers to evaluate traffic safety performance at signalized intersections.

Weigh-In-Motion (WIM) system tends to go out of calibration from time to time, as a result generate biased and inaccurate measurements. Several external factors such as vehicle speed, weather, pavement conditions, etc. can be attributed to such anomaly. To overcome this problem, a statistical quality control technique is warranted that would provide the WIM operator with some guidelines whenever the system tends to go 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.