Trajectory-based traffic sign visibility profiling using mobile LiDAR and driver-based viewpoints

Traffic signs represent the primary means of communication between road users and transportation engineers. Therefore, their visibility is critical to the safe and efficient operation of roads. Existing sign visibility assessments, however, are constrained by sparse observer positions, binary visibility classifications, and limited representation of how visibility evolves as a driver approaches a sign. These limitations restrict agencies’ ability to objectively assess occlusion severity, directly affecting maintenance prioritization. Building on prior trajectory and ray-tracing-based visibility studies, this paper proposes a mobile LiDAR framework for generating distance-resolved, area-based visibility profiles for roadside traffic signs. The contribution is a multi-dimensional visibility assessment that combines driver-based visibility profiling, sign-panel area quantification, panel-level occlusion mapping, and obstruction-source attribution to interpret visibility loss by severity and likely maintenance action. Specifically, the method quantifies whether a sign is visible, how much of its panel is visible, and how these conditions evolve as the driver approaches. In addition, the framework enables panel-level visibility mapping, allowing the spatial distribution of visible and obstructed sign regions to be evaluated along the approach. The framework was applied to 42 traffic signs along a 30-km rural corridor in British Columbia. Results indicate that line-of-sight obstructions frequently occur at intermediate and long distances, with the most pronounced visibility loss observed between 200 and 400 m. These patterns highlight the influence of roadway geometry, terrain, and roadside vegetation on sign visibility over the full approach path. To support method evaluation, LiDAR-derived visibility patterns and dominant occlusion sources were compared with independent street-level imagery. The comparison showed close agreement between simulated results and real-world driver perspectives for representative cases, supporting the reliability and interpretability of the framework. By providing quantitative, distance-resolved visibility metrics and spatial insight into partial sign occlusion, the proposed approach supports more objective traffic sign condition assessment and informed maintenance decision-making.