Development of a large-scale horizontal curve inventory for safety analysis using open GIS data

Abstract

Horizontal curves are crucial for the safety and mobility of roadways. To enhance the understanding of these curves, transportation agencies are investigating methods to accurately determine their spatial locations, geometric attributes, and conditions within their extensive road networks. Leveraging the widespread availability of Global Positioning System (GPS) and Geographic Information System (GIS) data, significant advancements have been made in developing automated algorithms that identify horizontal curves from GPS trajectories and GIS basemaps. This study introduces a novel dataset - the Horizontal Curve Inventory - that encapsulates detailed features of horizontal curves. We employed a modified automated cycle regression algorithm designed for large-scale road network analysis to extract horizontal curve data. This method was applied nationally to process an expansive network utilizing open-source GIS basemaps, encompassing over 300,000 miles across interstate, U.S. highway, and state highway systems. The inventory includes comprehensive data on curve radius, rotation angle, start and end point coordinates, and the curve's center points. These data are essential for supporting safety research pertaining to road alignment. Our approach demonstrated robust performance across various open GIS data sources and provides the first accurate, efficient, and comprehensive inventory of horizontal curves for all major highway systems in the United States. The dataset is available at http://aichengbo.com/research/national-horizontal-curve-inventory/.