An Automated Sound Barrier Inventory Method Using Mobile LiDAR

Abstract

A sound barrier, also called a noise barrier, plays an irreplaceable role in traffic noise abatement. The Federal Highway Administration’s (FHWA) highway noise regulation requires each state highway agency to maintain a complete inventory of all constructed noise-abatement features. Although key information for most of the newly constructed sound barriers has been inventoried, public transportation agencies are still struggling to keep close track of the in-service barriers because their inventory information is nonexisting, and manual inventory remains time-consuming, labor-intensive, and often dangerous. Therefore, many agencies have shown more interest in exploring the possibility of using light detection and ranging (LiDAR) data for assisting in sound barrier inventory, thanks to the widely available data set and much-improved data quality. This study proposes a LiDAR-based sound barrier inventory method to automatically extract the sound barrier’s location and measure the corresponding geometry. The extraction of a sound barrier is achieved using its unique features after random sample consensus (RANSAC)-based ground extraction and region-growing segmentation. The geometry measurement of the sound barrier is performed by analyzing the detailed dimension of the extracted point cloud, including location, height, length, and lateral offset. The experimental test conducted near Carver, Massachusetts showed the results with a precision rate of 99.9% and a recall rate of 93.8%. Moreover, the outcome of the experimental test has demonstrated the robustness of the proposed method in different complexities of the background and sound barrier types (linear and zigzag). This study has demonstrated the feasibility of using LiDAR for effectively inventorying in-service barriers. Besides the critical application of asset management, the detailed location and geometry information provided by the proposed method can provide valuable insight for other critical applications, such as traffic noise modeling.