# Differences Evaluation of Pavement Roughness Distribution Based on Light Detection and Ranging Data
## 6 Conclusions  
This study utilized a LiDAR device to obtain high-precision point cloud data, covering a distance of over 26 km in field tests. The acquired LiDAR data was processed through angle correction, coordinate transformation, and registration to extract the elevation data from multiple survey lines of the road section. The proposed method for calculating the pavement roughness using point cloud data was validated through a correlation test, comparing the calculated results with those obtained through total station measurements. Moreover, parametric and non-parametric tests were conducted to investigate the differences in the roughness distribution among multiple survey lines. Based on these analyses, the following conclusions can be drawn:  
1. Significant differences were observed in the IRI results among multiple survey lines within one road section. In particular, roads with an extensive uneven damage or partial widening may exhibit variations in their roughness grades;
2. The presence of significant differences in the IRI results among the different survey lines within one road section depends on the characteristics of the road. In summary, roads with a stronger dispersion of roughness tend to exhibit significant differences;
3. The difference between the detection length and the actual road length can lead to volatility in the repeated detection results. A significant difference in the roughness results may occur when the offset distance exceeds 30 m. Therefore, it is advisable to restrict the difference to 30 m to ensure the reliability of the results;
4. Traditional methods of calculating the IRI based on a single or small number of survey lines may not comprehensively characterize the road performance. Incorporating the IRI distribution attributes of different survey lines in the evaluation indexes is thereby recommended to improve the reliability of road performance assessments. \begin{table}
\begin{tabular}{c c} \hline
**Road Length (m)** & **Average Offset Distance Threshold (m)** \\ \hline
100–200 & 27.86 \\
200–300 & 26.87 \\
300–400 & 25 \\
400–500 & 27 \\ \textgreater{}500 & 29.47 \\ \hline \end{tabular}
\end{table} 
Table 5: Offset distance thresholds when the roughness changes significantly under different road lengths. This study proposed a novel method for measuring the pavement roughness using point cloud data, enabling the calculation of IRI results for any road profile, and providing insights into the spatial distribution of the pavement roughness. It offers road management departments more detailed data to assess driving quality and presents a new technique for road detection. Nevertheless, the analysis was limited to urban road data with IRI values ranging from 3-6 m/km without considering expressways, rural roads, and other scenarios. Future research will expand the data coverage and types into developing a more scientific, comprehensive, and practical model for evaluating the pavement roughness based on the detection results. Moreover, the LiDAR data, with its high precision and comprehensive characteristics, shows promise for further exploration in road performance assessments. In the future, it can be expanded to include the detection of pavement distress and roadside facilities, providing a more comprehensive understanding of the road infrastructure's conditions. The authors confirm their contribution to the paper as follows: Conceptualization, Q.G. and S.W. ; Data curation, Y.L. ; Formal analysis, Q.G. and S.W. ; Funding acquisition, Y.D. and C.L. ; Methodology, Q.G. ; Supervision, Y.D. and C.L. ; Validation, Q.G. and S.W. ; Writing--original draft, Q.G. ; Writing--review and editing, L.F., Y.L., Y.D. and C.L. All authors have read and agreed to the published version of the manuscript.