# Differences Evaluation of Pavement Roughness Distribution Based on Light Detection and Ranging Data
###### Abstract
LiDAR is an optical ranging method that helps obtain observed objects' position, distance, angle, and other parameters represented through a 3D point cloud output [16]. Unlike linear lidar, 3D LiDAR is less affected by light and has a broader detection range, allowing for converting 2D detection results into a 3D characterization. It improves the data dimension of pavement detection but dramatically increases the difficulty and consumption of data processing. Therefore, data processing has been the focus of the research conducted in this field. [PERSON] et al. adopted point cloud mapping and quadratic polynomial fitting to effectively separate the pavement boundary from the surrounding buildings and realized the automatic extraction of point clouds in the pavement area [17]. Based on the calculation results of road alignment, [PERSON] et al. developed a road slice segmentation method and a K-means clustering method to extract a survey line at a distance of 1 m from the outer road edge. Following this, they calculated the root-mean-square value, skewness, and kurtosis to evaluate the pavement roughness [18]. To obtain a broader range of LiDAR data, [PERSON] et al. collected high-altitude LiDAR data through the unmanned aerial vehicle (UAV). Moreover, the IRI results were calculated using the digital surface model (DEM) extracted from the point cloud data. However, due to the high altitude of these UAVs, the resolution of the pavement measurement points was found to be low, and the model accuracy was only about 75% [19]. With regard to roughness distribution, there are few studies. [PERSON] et al. used point cloud data and the DEM model to calculate airport runways' roughness and refined the elevation and slope variations of multiple survey lines [20]. Although this study considered the lateral influence of roughness distribution, it did not analyze whether the roughness index obtained under multiple survey lines made a significant difference. Recent advancements have demonstrated the reliability of LiDAR technology in processing point cloud data for pavement roughness measurements. However, due to the layout conditions of LiDAR and the principles governing point cloud data generation, obtaining direct elevation variations at the wheel track positions is challenging. Existing studies often calculate IRI results based on the elevation of the road centerlines or fixed-width survey lines from the centerline, thereby disregarding the influence of lateral roughness distribution. Nevertheless, it was found that lateral pavement roughness distribution could affect the vehicle excess fuel computation by up to 11% [21]. Therefore, a comprehensive evaluation of the lateral distribution of the pavement roughness is warranted. The present study introduces a novel method for measuring the pavement roughness using point cloud data, allowing for accurate evaluations of the international roughness index (IRI) of road surfaces and the characterization of their spatial roughness distribution. Further, parametric and non-parametric tests were conducted to examine for the differences in roughness distribution among multiple survey lines. The remainder of this paper is organized as follows. Section 2 present the data acquisition and processing details. The calculation method of the pavement roughness is introduced in Section 3. Section 4 illustrates the evaluation results of lateral pavement roughness. And Section 5 indicates the volatility evaluation results. Finally, Section 6 offers a summary of this research, and draws some implications for road maintenance.