The TRACFLOW software processes induction loop data to develop performance metrics for freeways in the Seattle area. The loop data are sometimes subject to errors. To find and correct errors, the TRACFLOW system uses a three-step approach to detect and address variations in the quality of the traffic data. Each step can include data replacement if sufficient supporting data are present. This combination of methods is automated whenever feasible to more efficiently handle the large data sets involved.
This report describes the three steps, detailing how each contributes to cleaner and more robust data sets. The objectives of these methods are to detect a higher percentage of anomalous data points, replace them with higher quality values, enable more of the data to be used, and increase overall automation of the process.