# Measurement Model of Full-Width Roughness Considering Longitudinal Profile Weighting
## 5 Experimental Verification and Analysis
### IRI Comparison
This comparative analysis strongly supported the feasibility and scientific validity of using point cloud data for road roughness evaluation. It is worth noting that, even when only extracting the elevations of the V\({}_{1}\) longitudinal profile from the point cloud data, stable IRI calculation results could be obtained. This stability was attributed to a key step in point cloud data processing: the precise determination of the V\({}_{1}\) longitudinal profile position within the lane, achieved based on predefined lane boundaries. Figure 10: Comparison of the IRI based on 3 longitudinal profile elevations. By comparison, the actual measurement trajectory of the vehicle-mounted laser profiler exhibited a certain degree of randomness during practical operation, which affected the stability and reliability of its measurement results. Therefore, the adoption of 3D point cloud technology for road roughness evaluation demonstrated superior technical advantages and practicality. Furthermore, the variability of the IRI\({}_{\text{F}}\), represented by \(Cv_{\text{F}}\), serves as an indicator of the performance stability and reliability of the measuring device. Specifically, if the 3D measurement system has deficiencies in the collection and processing of road surface elevation data, it will be difficult to ensure a low \(Cv_{\text{F}}\). This is because the equivalent elevation \(P_{4}(n)\) is derived by integrating all elevation data from the \(n\)-th cross-section. Abnormal data on this cross-section can alter the accuracy of \(P_{4}(n)\) without affecting the elevation of the V\({}_{1}\) longitudinal profile. Thus, the equivalent elevation \(P_{4}(n)\) provides a comprehensive and detailed description of the road surface morphology. By contrast, the IRI\({}_{\text{V}}\) indicator, which relies solely on the V\({}_{1}\) longitudinal profile from point cloud data, appeared to be more one-sided. This is because it only reflects the roughness of that specific profile and cannot fully represent the entire road surface condition. Therefore, when conducting road roughness analysis, particularly when the road elevation data comes from 3D point clouds, it is more reasonable to use the full-width roughness IRI\({}_{\text{F}}\) as an evaluation metric. IRI\({}_{\text{F}}\) not only provides a more accurate representation of the overall road performance but its variability can also serve as an important reference for evaluating the measurement performance of the instrumentation. It should be noted that full-width roughness detection methods, while providing more comprehensive pavement information, typically incur higher device costs due to their reliance on advanced technology and complex hardware. Moreover, the processing of extensive point cloud data may not be as computationally efficient as simpler traditional devices, which can quickly produce results albeit with potentially lower accuracy and coverage. Thus, despite its advantages in detail, full-width detection may not rival the economic efficiency and ease of operation of classic, cost-effective detection equipment.