A Non-Contact Method for Detecting and Evaluating the Non-Motor Use of Sidewalks Based on Three-Dimensional Pavement Morphology Analysis.

This study proposes a non-contact framework for evaluating the skid resistance of shared roadside pavements to improve cyclist and pedestrian safety. By integrating a friction tester and a laser scanner, we synchronize high-resolution three-dimensional (3D) surface texture characterization with friction coefficient measurements under dry and wet conditions. Key metrics-including fractal dimension (), macro/micro-texture depth density ( and ), mean texture depth (), and joint dimensions-were derived from 3D laser scans. A hierarchical regression analysis was employed to prioritize the influence of texture and joint parameters on skid resistance across environmental conditions. Combined with material types (brick, tile, and stone) and drainage performance, these metrics are systematically analyzed to quantify their correlations with skid resistance. Results indicate that raised macro-textures and high (>2.5) significantly enhance dry-condition skid resistance, whereas recessed textures degrade performance. The hierarchical model further reveals that and dominate dry friction ( = 0.61 and -0.53, respectively), while micro-texture density () and seam depth are critical predictors of wet skid resistance ( = -0.76 and 0.31). In wet environments, skid resistance is dominated by micro-texture density ( < 3500) and macro-texture-driven water displacement, with higher values indicating denser micro-textures that impede drainage. The study validates that non-contact laser scanning enables efficient mapping of critical texture data (e.g., pore connectivity, joint depth ≥0.25 mm) and friction properties, supporting rapid large-scale pavement assessments. These findings establish a data-driven linkage between measurable surface indicators (texture, morphometry, drainage) and skid resistance, offering a practical foundation for proactive sidewalk safety management, especially in high-risk areas. Future work should focus on refining predictive models through multi-sensor fusion and standardized design guidelines.