A Study on the Method for Cleaning and Repairing the Probe Vehicle Data

Probe vehicle data are being increasingly applied in urban dynamic traffic data collection. However, the mobility and scale limit of probe vehicles may lead to incomplete or inaccurate data and thus influence the measurement of the state of traffic. At present, probe vehicle data are usually repaired by linear interpolation or a historical average method, but the repair accuracy is relatively low. To address the given problems, the multithreshold control repair method (MTCRM) was proposed to clean and repair the probe vehicle data. The MTCRM adopts threshold control and a rule based on the approximate normalization transform to clean abnormal traffic data and to fill in the missing data by a weighted average method and an exponential smoothing method. In this approach, we combine topological road network characteristics to fill in the missing data from data for neighboring road sections and repair noisy data by reconstructing the principal components. This paper mainly focuses on analyzing the component of the recurring pattern of probe vehicle data, which can provide guidelines for the subsequent traffic forecasts. The findings of data repair for different grades of road in Beijing, China, demonstrate that the mean repair error may meet the requirements of traffic-state measurement, demonstrating that MTCRM can effectively clean probe vehicle data.