# Differences Evaluation of Pavement Roughness Distribution Based on Light Detection and Ranging Data
###### Abstract  
Pavement roughness serves as a crucial indicator for evaluating road performance. However, traditional measurement methods, such as laser detection vehicles, are limited to providing roughness values for a single profile, failing to capture the overall pavement condition comprehensively. To address this limitation, this study utilized high-precision light detection and ranging technology (LiDAR) to acquire three-dimensional point cloud data for a 25 km road section in Shanghai. Road elevations were extracted from different lateral survey lines. Subsequently, variance analysis and the Kruskal-Wallis non-parametric test were conducted to evaluate the differences in the lateral distribution and longitudinal variability of the pavement roughness. The findings revealed significant differences in the international roughness index (IRI) among the survey lines within the road section. Moreover, the observed variations in the lateral distribution of pavement roughness were influenced by the characteristics of the road section itself. Roads exhibiting discrete roughness patterns displayed a higher likelihood of significant detection disparities. Additionally, it was discovered that the discrepancy between the detection length and the actual road length introduced volatility in repeated detection results, necessitating a limitation of this discrepancy to 30 m. Consequently, it has been recommended to consider the lateral distribution of pavement roughness and to regulate the detection length in road performance evaluations to enhance reliability and facilitate more accurate maintenance decision making. The study highlights the importance of incorporating comprehensive assessment approaches for pavement roughness in road management practices. highway transportation; pavement roughness measurement; LiDAR data analysis; hypothetical test 2023138080  
to its fundamental principles, measurements of pavement roughness are categorized into three methods: subjective rating panels, direct profile measurements, and response-type road roughness measurements (RTRRMS) [6]. The subjective rating panels adopt expert grading, which is highly subjective, and is used primarily as a reference for road evaluation. Direct profile measurements detect the elevation variation of the road surface along the driving track of a vehicle. Precise lasers, such as laser profilers [7] and 3D LiDAR [8], are deployed on specialized detection vehicles, with the IRI determined directly from the longitudinal profile elevation. This method exhibits a high accuracy and is the most widely used for evaluating the pavement roughness. As to the RTRRMS, it is used to measure the pavement roughness by collecting the vibration responses of vehicles, such as the bump integrator [9], the Bureau of Public Roads (BPR) roughometer [10], accelerometers [11], and smartphones [12]. This method has a low cost and a high efficiency, thereby making it a helpful supplement to direct profile measurements. However, it is sensitive to sudden changes and susceptible to the vehicle parameters, driving environment, and operating state. As mentioned above, these three measurements calculate the pavement roughness by detecting the elevation variations of one or several survey lines [13]. However, they cannot fully capture the overall condition of the road's surface. While road elevation variations follow a statistically zero-mean normal distribution [14], the lateral distribution of the elevations is not uniform due to the effects of vehicle rolling, road cross and longitudinal slope, and weather conditions. When different detection vehicles traverse a given road section, the detection track of the laser probes is difficult to replicate completely, resulting in a low consistency of the IRI detection results, which ultimately impacts the evaluation of large-scale pavement roughness measurements. In recent times, light detection and ranging (LiDAR) technology has been rapidly developing and finding more frequent use in pavement detection [15].