Lidar Data-Based Method to Measure Sight Distance at Unsignalized Intersection

Accurate assessment of intersection sight distance (ISD) is crucial for ensuring safety at unsignalized intersections. Traditional field measurement methods are labor-intensive, time-consuming, and susceptible to human error, while previous lidar-based approaches often rely on mobile lidar scanning systems, which have limited data coverage. In this study, a novel methodology is proposed, which utilizes airborne lidar scanning data to evaluate ISD automatically at unsignalized intersections. The method involves generating a digital surface model and conducting line-of-sight analyses while allowing users to customize such parameters as observer position, vehicle type, and vegetation exclusion. In the study, the proposed method was applied to 13 intersections in Auburn, Alabama, and the results were validated against traditional field measurements. The comparison revealed that discrepancies mainly stemmed from human error in field assessments, dynamic changes in the environment, and the time lag between lidar data collection and field evaluation. Despite these challenges, the lidar-based method proved to be a cost-effective, scalable, and reliable tool for assessing ISD, offering significant benefits for transportation agencies in planning and prioritizing intersection safety improvements. Future research should be focused on integrating additional data sources, automating data input processes, and addressing dynamic environmental factors to further enhance the methodology’s accuracy and applicability.