IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i16p9259-d616623.html
   My bibliography  Save this article

Safety Assessment of Urban Intersection Sight Distance Using Mobile LiDAR Data

Author

Listed:
  • Omar Kilani

    (Department of Civil & Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Maged Gouda

    (Department of Civil & Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Jonas Weiß

    (Department of Electrical and Computer Engineering, Technical University of Munich, 80333 Munich, Germany)

  • Karim El-Basyouny

    (Department of Civil & Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

Abstract

This paper proposes an automated framework that utilizes Light Detection and Ranging (LiDAR) point cloud data to map and detect road obstacles that impact drivers’ field of view at urban intersections. The framework facilitates the simulation of a driver’s field of vision to estimate the blockage percentage as they approach an intersection. Furthermore, a collision analysis is conducted to examine the relationship between poor visibility and safety. The visibility assessment was used to determine the blockage percentage as a function of intersection control type. The safety assessment indicated that intersections with limited available sight distances (ASD) exhibited an increased risk of collisions. The research also conducted a sensitivity analysis to understand the impact of the voxel size on the extraction of intersection obstacles from LiDAR datasets. The findings from this research can be used to assess the intersection without the burden of manual intervention. This would effectively support transportation agencies in identifying hazardous intersections with poor visibility and adopt policies to enhance urban intersections’ operation and safety.

Suggested Citation

  • Omar Kilani & Maged Gouda & Jonas Weiß & Karim El-Basyouny, 2021. "Safety Assessment of Urban Intersection Sight Distance Using Mobile LiDAR Data," Sustainability, MDPI, vol. 13(16), pages 1-22, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:9259-:d:616623
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/16/9259/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/16/9259/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:9259-:d:616623. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.