Remote Sightline Inspection at Highway-Rail Grade Crossings Using LiDAR Data and Geospatial Analysis

Abstract: In 2022, the United States recorded 2,034 incidents at Highway-Rail Grade Crossings (HRGCs), resulting in serious risks such as injuries, fatalities, and substantial property damage. This high incident rate highlights an urgent need for more effective strategies to prevent and mitigate such risks. The safety challenge is intensified by the sheer number of crossings, over 212,000 public and private HRGCs nationwide, and the limitations of traditional safety inspections, which rely heavily on manual assessments. Additionally, obstructed sightlines at these crossings pose significant risks, restricting road users’ ability to detect oncoming trains. While existing studies have explored behavioral factors and general safety issues, there is a notable gap in the literature regarding quantitative assessments of sightline visibility at HRGCs. This study seeks to fill this gap by employing remote sensing techniques to analyze and quantify sightline visibility at HRGCs. We examined 12 specific HRGCs through a geospatial analysis approach in ArcGIS Pro, utilizing Viewshed and Observer Points analysis. This methodology integrated Light Detection and Ranging (LiDAR) data from the United States Geological Survey (USGS) with crossing information from the Federal Railroad Administration (FRA) to produce detailed sightline evaluations. The results reveal that the primary factors contributing to limited sightlines in our case studies include traffic control devices, environmental elements, and geometric conditions of the crossings. Moreover, the findings suggest that using USGS LiDAR data and geospatial analysis tools offers a promising avenue for quickly and cost-effectively identifying sightline visibility issues at HRGCs, potentially supporting targeted safety interventions.