1. Home
  2. RiP
  3. View Record
http://2019.itf-oecd.org/

Using Advanced Analytics to Frame Vulnerable Road User Scenarios with Autonomous Vehicles

Connected and automated vehicle (CAV) technologies can dramatically improve safety by reducing human errors, which contribute substantially (an estimated 94 percent) to roadway crashes. CAVs can eventually operate effectively on roadways without experiencing decreased performance due to distraction or fatigue. However, technological advances will not uniformly decrease crash risks. Some environments, crash types, and user groups will continue to experience elevated risks particularly vulnerable road users such as pedestrians. This project will address these critical safety issues by (1) assessing the current and future landscape of pedestrian and vehicle conflicts, (2) identifying how vehicle technology, planning policies, and data analytics can provide systemic solutions to pedestrian-vehicle conflicts, and (3) using big data analytics from vehicle-to-pedestrian, vehicle-to-vehicle and vehicle-to-infrastructure communications to identify dangerous pre-crash behaviors. This trans-disciplinary and multi-modal approach is critical because solutions require insights from multiple fields. For example, data science provides ideas on how to use data from CAVs to manage the system and identify conflict situations; travel behavior provides insights into how travel patterns will change in the future and how this will affect risk profiles; and planning offers lessons about how to design transportation infrastructure to reduce risks that technology alone cannot ameliorate. The specific tasks will include literature reviews on current patterns of pedestrian-vehicle conflicts, assessment of how planning and physical design strategies can reduce pedestrian-CAV conflicts, analysis of time use (American Time Use Survey [ATUS]) and travel survey data (National Highway Traffic Safety Administration [NHTS]) to assess mobility trends. Furthermore, risk analysis will be conducted based on analysis of case studies from Ann Arbor, MI (available through Research Data Exchange) and assessment of how automated vehicle technology will impact crash risk and potential countermeasures. The team will analyze safety data and propose a framework to link automation technology to human error/crash typologies. Overall, the study will apply innovative statistical, artificial intelligence, and visualization tools to extract valuable information from data, with the purpose of improving safety across modes, especially for vulnerable road users.

Language

  • English

Project

  • Status: Active
  • Funding: $340,000
  • Contract Numbers:

    69A3551747113

  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Collaborative Sciences Center for Road Safety

    University of North Carolina, Chapel Hill
    Chapel Hill, NC  United States  27514
  • Project Managers:

    Sandt, Laura

  • Performing Organizations:

    University of North Carolina - Chapel Hill

    Chapel Hill, NC  United States  27599

    University of Tennessee, Knoxville

    Center for Transportation Research
    Conference Center Building
    Knoxville, TN  United States  37996-4133
  • Principal Investigators:

    McDonald, Noreen

    Khattak, Asad

  • Start Date: 20170301
  • Expected Completion Date: 20181231
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01627994
  • Record Type: Research project
  • Source Agency: Collaborative Sciences Center for Road Safety
  • Contract Numbers: 69A3551747113
  • Files: UTC, RiP
  • Created Date: Feb 27 2017 9:52AM