Advancing Road Infrastructure: Cross-Slope Analysis of Road Sections Using a Mobile Mapping System (MMS)

Digitalization of road infrastructures involves the use of advanced technologies to collect and analyze data about pavement conditions. In this regard, Mobile Mapping Systems (MMSs), typically deployed on mobile platforms such as vehicles, have gained more attention in road detection and mapping applications. These innovative systems exploit a combination of various sensors and technologies (i.e., Global Navigation Satellite System (GNSS), Light Detection and Ranging (LiDAR) scanners, high-resolution cameras, and Inertial Measurement Units (IMU)) to gather geospatial data, including detailed point clouds. In this study, as a significant stride towards optimizing road infrastructure management, point cloud data generated by a MMS during field surveys have been processed to extract specific information, such as cross-slope. Linear regression analysis was then used to derive the cross-slopes of 24 sections. Comparisons were made on the same sections using a contact profilometer in order to validate the results. Data points cluster near the line of equality highlighting a strong correlation between the two sets of measurements and across different slope ranges. The analysis further delved into the error variability across slope conditions, categorizing them into three ranges and calculating the average error within each, to assess the consistency and reliability of the instruments under varying conditions. Finally, the findings highlighted the efficacy of MMS technologies in road infrastructure management, demonstrating their capability to detect surface irregularities too. This study emphasizes the significance of using these monitoring technologies for infrastructure by highlighting the possibility of also detecting information about the surface geometry (e.g., cross-slope) that affects road safety.