A Visibility and Safety Assessment of Urban Intersection Sight Distance using mobile LiDAR ‎Data

  • Author / Creator
    Kilani, Omar
  • This thesis focuses on intersection sight distance design, which is an extremely critical element in ‎urban road design. Research has shown that providing adequate sight distance contributes to safe ‎operation at intersections. Conversely, a restricted sight distance impacts drivers’ fields of vision ‎and reduces their ability to complete certain maneuvers smoothly and safely. The existence of ‎obstacles within the intersection sight triangle limits drivers’ visual fields and blocks their ability ‎to see and react to vehicles on the crossing road. These obstacles could be buildings, structures, ‎trees, bushes, or any roadside object. However, locating these obstacles through site visits is ‎generally not an efficient way to address such issues since this is a tedious process, labor-intensive, and exposes the workforce to high safety risks. It requires surveying equipment and, in ‎some cases, road closures. Also, to simply identify obstacles does not account for the different ‎types of modes traveling through the intersection and could lead to inaccurate outcomes. ‎
    Several researchers have attempted to study the sight distance and identify the obstructions at ‎intersections; however, previous work in this area is very scarce and involves a manual process, ‎especially at urban intersections. Therefore, the first part of this thesis focuses on developing a ‎fully automated method using mobile LiDAR point cloud to extract the information about the ‎road objects that impact drivers’ vision by conducting a visibility assessment. This part includes ‎‎(a) detecting the obstacles to estimate the blockage percentage and a visualization of the ‎obstruction from a driver’s perspective, (b) conducting a sensitivity analysis to study the impact ‎of different voxel size on visibility assessments, and (c) investigate the impacts of operating ‎speeds on drivers’ fields of vision and intersection visibility. ‎
    The second part of this research conducts a safety-based assessment, using the LiDAR dataset to ‎study the relationship between the collisions and the visibility information extracted at the urban ‎intersections. Understanding the root cause of a visibility problem is crucial for improving safety ‎at intersections and achieving the goals of Vision Zero. The collision analysis was performed ‎using the Empirical Bayesian technique to identify intersections that exhibited an over-representation of specific collision patterns caused by visibility problems. The results show that ‎intersections with limited available sight distances due to road obstacles exhibited an increased ‎risk of collisions. This would significantly help road safety agencies prioritize and rank the ‎hazardous intersections for potential treatment and select cost-effective countermeasures. ‎

  • Subjects / Keywords
  • Graduation date
    Fall 2021
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.