Impact of Connected Vehicle Technology on Traffic Safety under Different Highway Geometric Designs

Connected and automated vehicle (CAV) driving features can impact traffic safety in many aspects owing to their improved driving behavior. On the other hand, road geometric design elements are mainly based on human reactions and behavior, which might affect safety depending on road layout and the parties involved. However, automation and connectivity can convey more data about the driving environment that will reduce confronting unexpected driving conditions and driving load on drivers. Therefore, the risk of crashes due to roadway geometries will be reduced. The main objective of this study is to focus on the performance of the traffic flow, including CAVs with different geometric designs addressing the potential crash spots. This study aims to determine the efficacy of CAVs on traffic network safety quantitively and qualitatively. For this purpose, multiple scenarios with different geometric features are designed and simulated. Simulations include varied CAV shares in traffic composition and employ driving features of CAVs. Using the surrogate safety assessment model (SSAM), simulation results are evaluated for potential conflicts. Crash severity, frequency, and classification are studied to determine the safety effects of CAVs on potential crash hot spots. Results indicated that higher penetration rates of CAVs could improve the safety performance of traffic networks in multiple cases by reducing deceleration rates, cooperative lane changing, and adjusted speed in required situations. However, due to the interaction of CAVs and human-driven vehicles (HDVs) in a signalized intersection, safety performance might not benefit from CAV presence.

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  • Supplemental Notes:
    • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program. Title page date: September 2022.
  • Corporate Authors:

    University of Utah, Salt Lake City

    Department of Civil and Environmental Engineering
    Salt Lake City, UT  United States 

    Mountain-Plains Consortium

    North Dakota State University
    Fargo, ND  United States  58108

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Authors:
    • Azin, Bahar
    • Wang, Qinzheng
    • Yang, Xianfeng
    • Gong, Yaobang
  • Publication Date: 2021-10


  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Figures; Maps; Photos; References; Tables;
  • Pagination: 67p

Subject/Index Terms

Filing Info

  • Accession Number: 01864874
  • Record Type: Publication
  • Report/Paper Numbers: MPC-22-484
  • Contract Numbers: MPC-590
  • Created Date: Nov 22 2022 10:16AM