Methodology for Correcting Erroneous Individual Vehicle Speed Data Using Locally Weighted Regression

摘要

Effective detection and correction of outliers in raw traffic data collected from the field is ofkeen interest because reliable traffic information is highly dependent on the quality of rawdata. Global positioning systems (GPS) based traffic surveillance systems are capable ofproducing individual vehicle speeds that are invaluable for various traffic management andinformation strategies. This study proposes a locally weighted regression (LWR) basedfiltering method for individual vehicle speed data. Both a technique to generate syntheticoutliers and to injecting synthetic outliers are also presented to systematically evaluate theproposed method. A method to determine parameters associated with the LWR that affect thesmoothing performance is devised and applied to obtain more reliable data correction. A set ofillustrative evaluation examples explains that the proposed method is useful in filteringindividual vehicle speed data.