Automated Assessment of Passing Sight Distance on Rural Highways using Mobile LiDAR Data

Abstract

Providing sufficient Available Sight Distance (ASD) that meets the minimum design requirements is crucial for highway safety. Previous work on sight distance assessment focused on Stopping Sight Distance (SSD) with little attention given to Passing Sight Distance (PSD). Insufficient PSD could lead to severe collisions such as head-on and sideswipe crashes. To address this gap, this paper introduces an automated method for PSD assessment on two-lane highways using mobile Light Detection and Ranging (LiDAR) data. The procedure involved extracting centerline lane marking, defining passing-allowed and passing-prohibited regions, computing the ASD, and comparing the existing centerline marking pattern (i.e., passing and no-passing zones) to a proposed lane marking that is based on the ASD for passing maneuvers. Regions that meet the design standards, substandard zones, and non-optimal design regions were all defined. A reallocation of PSD zones was conducted based on the ASD including modifying the existing lane marking pattern, which resulted in increasing the total length of passing zones by up to 20%, providing more, but safer, passing opportunities. A high-level safety assessment of historical collisions showed clusters of crashes along regions where passing is currently allowed at locations where the ASD is less than standard requirements. The proposed framework represents a tool by which transportation agencies could assess PSD, upgrade the design of existing highways, and investigate the consequences of PSD limitations to ensure compliance with standards during highway service life.

Safety Assessment of Urban Intersection Sight Distance Using Mobile LiDAR Data

“…To address this, a sensitivity analysis was conducted using various voxel sizes. While voxel size does not significantly impact this sight distance application, it may be very sensitive to extract different geometric road features in other transportation applications [42]. In other words, the selection of an inappropriate voxel size leads to minor inaccuracies in detecting obstacles.…”

Automated Object Detection, Mapping, and Assessment of Roadside Clear Zones Using Lidar Data

“…In addition, the collected information can be mapped with road collisions in geographic information system software to further study the relationship between crashes and existing roadside parameters. The collision analysis can also include other road design features such as sight distances and horizontal curves information (40)(41)(42). As such, the process can help develop performance-based design guidelines for roadside clear zones and support decision making on road maintenance and upgrades.…”

A Review of Mobile Mapping Systems: From Sensors to Applications

“…Typically, road condition monitoring process consists of four steps [148]: 1) data collection using MMS, 2) defect detection, which can be performed automatically using deep learning-based approaches, 3) defect assessment, and 4) road condition index calculation to classify road segments based on the type and severity of the defect. Therefore, MMS data can further help in increasing the safety of the road, for example, by detecting road potholes [78,149], evaluating the location of speed signs before horizontal curves on roadways [150], or assessing the passing sight distance on highways [151]. Building Information Modeling: BIM is one of the most well-established technologies in the industry of architecture, engineering, and construction.…”