3D virtual intersection sight distance analysis using lidar data

Sight distance analyses require careful and detailed field measurements to facilitate proper engineering decision making regarding the removal of obstructions, establishment of regulatory and advisory speed limits, and the location of new access points, among numerous other purposes. However, conventional field measurements for these analyses present safety concerns because they require personnel to be in or adjacent to traffic lanes. They can also be time consuming, costly, and labor intensive. Furthermore, the predominantly two-dimensional (2D) methods involve simplifying assumptions such as a 'standard' vehicle heights and lengths without considering the wide range of vehicles and drivers present on the road. Recently, many transportation agencies worldwide have begun to acquire mobile lidar data to map their roadway assets. These data provide a rich three-dimensional (3D) environment that enables one to virtually visit a site at any frequency and efficiently evaluate sight distances from the safety of the office. This study investigates advanced safety analysis methodologies for drivers’ sight distance based on high resolution lidar data. The developed simulation method enables users to virtually evaluate available sight distances in a 3D context considering a variety of objects, vehicle types, and multi-modal forms of transportation (e.g., bicycle, pedestrian). The feasibility of this technique was analyzed with a case study at an intersection located in Corvallis, Oregon, USA. The experimental results demonstrated the ability of the proposed methodology to capture significantly more detail on visibility constraints when compared with conventional measurements as well as provide more flexibility in the analysis.