Data Calibration Based on Multisensor Using Classification Analysis: A Random Forests Approach

Abstract

This paper analyzes the problem of meaningless outliers in traffic detective data sets and researches characteristics about the data of monophyletic detector and multisensor detector based on real-time data on highway. Based on analysis of the current random forests algorithm, which is a learning algorithm of high accuracy and fast speed, new optimum random forests about filtrating outlier in the sample are proposed, which employ bagging strategy combined with boosting strategy. Random forests of different number of trees are applied to analyze status classification of meaningless outliers in traffic detective data sets, respectively, based on traffic flow, spot mean speed, and roadway occupancy rate of traffic parameters. The results show that optimum model of random forest is more accurate to filtrate meaningless outliers in traffic detective data collected from road intersections. With filtrated data for processing, transportation information system can decrease the influence of error data to improve highway traffic information services.