Statistical Methods for Detecting Spatial Configuration Errors in Traffic Surveillance Sensors

Abstract

With large-scale deployment of traffic surveillance sensors becoming commonplace, it becomes critical to maintain correct information about the spatial configuration of the sensors. The problem is burdensome when hundreds or thousands of sensors are deployed. One common configuration error is the switching of directions of highway loop detectors that share the same cabinet. Proposed are semiautomatic and automatic methods for detecting such errors, on the basis of the strong correlations between measurements made by spatially close sensors. The semiautomatic method uses a multidimensional scaling (MDS) map of sensors, which visually displays the similarity between sensor measurements and enables one to easily identify sensor mislabeling. The automatic method uses a scoring scheme that computes the probability of sensor mislabeling from the pairwise distance or similarity matrix. The algorithm, tested on data from a four-lane freeway consisting of 64 sensor locations—10 of which had switched locations—successfully detected all errors with 5.6% false detection rate, even with poor data quality. The MDS map can be used for other applications, such as detection of sensor malfunctions.