Evaluating the impact of factors in vehicle based pavement sensing implementation: sensor placement, pavement temperature, speed, and threshold

Abstract

The purpose of the paper is to improve the efficiency of vehicle based sensing technology in highway pavement condition assessment by evaluating the effect of four factors (sensor placement, pavement temperature, drive speed, and threshold for pavement distress classification) and providing suggestions to better improve the accuracy of pavement condition detection and minimize the interruption of pavement sensing operation. Two I-10 corridors in the Phoenix region were selected for vibration data collection and data analysis. A series of statistical analyses were performed to determine if each one of the factors has a significant impact on the pavement distress detection. The results of Analysis of Variance (ANOVA) tests and Analysis of Covariance (ANCOVA) tests show that the placement of sensors have a significant effect in the pavement condition assessments. The significant differences occurred in the group of sensors that were placed on the same side of the vehicle, as well as, in either front wheels or rear wheels of the vehicle. The effect of pavement temperature on the vehicle based sensing implementation is significant while the mean drive speed is not seen as a significant factor in the pavement condition survey. The two thresholds were determined to select points of interest (POI; cracks, potholes) for the pavement distress classification and these POIs are in good agreement with international roughness index (IRI) data in an ArcGIS map. The findings of the paper can be used to better improve the computing algorithms of vehicle based sensing techniques.