A GIS and LiDAR-Based Method for Automated Sight Distance Measurement and Quantitative Safety Assessment

Abstract

Intersection Sight Distance (ISD) plays a critical role in ensuring roadway safety, particularly at unsignalized intersections where the lack of adequate visibility can result in severe accidents. Traditional ISD assessment methods often rely on manual measurements, which can be time-consuming, costly, and prone to inaccuracies. This study aims to address these limitations by developing an automated GIS-based tool that leverages LiDAR data to evaluate ISD at two-way stop-controlled (TWSC) intersections in Alabama. This tool facilitates efficient screening of large road networks, calculates the recommended ISD, and identifies sight distance obstructions. Furthermore, the study investigates the relationship between ISD and crash frequency, focusing on the unique traffic conditions in Alabama. Comprehensive LiDAR data covering 230 intersections were processed, and historical crash data from 2018 to 2022 were analyzed to identify target crashes influenced by sight distance deficiencies. The study presents the development of ISD assessment tools, application of methodologies for evaluating ISD impact on intersection safety, and the formulation of region-specific Crash Modification Factors (CMFs). The findings provide actionable insights for optimizing intersection design to enhance roadway safety and guide future ISD-related research.