# Measurement Model of Full-Width Roughness Considering Longitudinal Profile Weighting
## 5 Experimental Verification and Analysis
### Experimental Data Analysis
However, standard blocks arranged at other locations (longitudinal profiles) could not be displayed by these two longitudinal profiles. This also proved the insufficiency of using elevation values of a single longitudinal profile to represent the performance of the entire lane. Figure 6: Original road elevation point cloud data \(P_{1}\). Figure 8 illustrates the cumulative elevation curve \(P_{3}(n)\) for the 600 m long lane. Upon careful observation, several distinct peaks could be identified along the direction of travel, specifically within the ranges of 200 m to 300 m, 300 m to 400 m, and 500 m to 600 m. These peaks corresponded to the multiple sets of standard test blocks placed on the test road. Excluding the peaks induced by the standard test blocks, the cumulative elevation curve \(P_{3}(n)\) remained predominantly within the range of 9750 mm to 11,250 mm. This indicated that, apart from the areas where the test blocks were laid, the overall elevation changes of the lane were relatively stable, with a mean value of approximately 10,645 mm. From a quantitative perspective, the maximum peaks caused by the standard test blocks in the elevation curve \(P_{3}(n)\) exceeded the average value by more than 750 mm, a significant deviation from the actual thickness of the test blocks, which was less than or equal to 20 mm. This discrepancy could be attributed to the fact that the cumulative elevation \(P_{3}(n)\) is obtained by accumulating all of the weighted elevations \(P_{2}(x_{i},n)\) of the \(n\)-th cross-section. Additionally, the cumulative elevation \(P_{3}(n)\) is directly related to the sampling density of the point cloud. While it is possible to calculate specific and precise values for road surface roughness indicators such as the IRI directly using the elevation sequence \(P_{3}(n)\) from Figure 8, these values may exceed cognitive ranges and fail to provide accurate and practical guiding suggestions for road management authorities. Figure 9 illustrates the curves of V\({}_{1}\) longitudinal profile elevation from Figure 6 and equivalent elevations \(P_{4}(n)\) with respect to the driving distance. As is evident from the figure, the amplitude of \(P_{4}(n)\) was significantly reduced, and it aligned with the elevation of the V\({}_{1}\) longitudinal profile. This data processing method effectively suppressed elevation  
Figure 7: The weighted elevation point cloud data \(P_{2}\). fluctuations, enabling a more accurate representation of the actual terrain characteristics. Furthermore, the scaling operation not only adjusted the elevation data but also skillfully preserved the elevation mutation points caused by the placement of standard test blocks on the actual road. This unique feature is particularly crucial in analyzing the dynamic response of vehicles to road roughness and calculating road roughness indicators, as it ensures that important road feature information is not omitted or mistakenly deleted during data preprocessing. This has profound implications for subsequent engineering analyses, pavement quality evaluations, and optimization of vehicle driving performance.