Abstract:

The quality and safety of road networks are of paramount importance in modern transportation infrastructure. Road surface conditions, particularly road roughness, significantly impact vehicular travel safety, user comfort, vehicle operating costs, and overall road infrastructure maintenance. Traditional methods for road roughness analysis, such as manual inspections or image annotation, often present limitations in terms of data completeness, efficiency, and cost-effectiveness, especially for extensive road networks. This study investigates the potential of Unmanned Aerial Vehicles (UAVs) equipped with Structure-from-Motion (SfM) derived point clouds to transform road roughness assessment. By leveraging the capabilities of UAVs, including rapid data acquisition and high-resolution imagery, and employing SfM to generate detailed point clouds, this research aims to provide a comprehensive analysis of road surface conditions. The study, conducted on a road segment within the Harran University Osmanbey campus, systematically examines road roughness at different kernel sizes: 30 cm (smaller), 50 cm (moderate), and 75 cm (larger). Through this investigation, insights are gained into how different scales of analysis influence roughness measurements. The findings highlight the potential of UAV-derived point clouds as a promising avenue for road roughness analysis, offering transportation authorities and road administrators an efficient and cost-effective means of maintaining and enhancing road networks. The integration of this technology could lead to the development of safer, more efficient, and economically sustainable road transportation systems, benefiting both road users and infrastructure managers. As research and technological advancements in UAV-based road roughness assessment continue to progress, the potential for revolutionizing road management practices becomes increasingly apparent, ultimately leading to improved road quality and enhanced travel experiences for road users.

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