Detection and Correction of Inductive Loop Detector Sensitivity Errors by Using Gaussian Mixture Models

Abstract

Inductive loop detectors (ILDs) form the backbone of many traffic detection networks by providing vehicle detection for freeway and arterial monitoring as well as signal control. Unfortunately, ILD technology generally has limited the available sensitivity settings. Changing roadway conditions and aging equipment can cause ILD settings that had been correct to become under- or oversensitive. ILDs with incorrect sensitivities may result in severe errors in occupancy and volume measurements. Therefore, sensitivity error identification and correction are important for quality data collection from ILDs. In this study, the Gaussian mixture model (GMM) is used to identify ILDs with sensitivity problems. If the sensitivity problem is correctible at the software level, a correction factor is then calculated for the occupancy measurements of the ILD. The correction methodology developed in this study was found effective in correcting occupancy errors caused by the ILD sensitivity problems. Single-loop speed calculation with the corrected occupancy increases the accuracy by 12%. Since this GMM-based approach does not require hardware changes, it is cost-effective and has great potential for easy improvement of archived loop data quality.