From Data to Action: Leveraging Machine Learning/ Artificial Intelligence to Guide Proactive Pedestrian and Cyclist Safety Initiatives

Road traffic crashes have become a modern curse in our society. Every day, almost 3,700 people are killed globally in crashes involving cars, buses, motorcycles, bicycles, trucks, or pedestrians. More than half of those killed are vulnerable road users such as pedestrians, motorcyclists, or cyclists (WHO, 2018). Proactive safety measures, when implemented in the correct ways, can save thousands of these lives. This research tries to offer a data-driven solution for the safety of these vulnerable road users. The continued assessment of mitigating crashes and reducing severity allows us to invest strategically in proven, well vetted strategies. The overarching goal of this research is to improve pedestrian and cyclist safety in Ohio by leveraging advanced analytics to gain new insights from crash data.


  • English


  • Status: Active
  • Funding: $100000
  • Contract Numbers:




  • Sponsor Organizations:

    Federal Highway Administration

    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590
  • Managing Organizations:

    Ohio Department of Transportation

    Research Program
    1980 West Broad Street
    Columbus, OH  United States  43223
  • Project Managers:

    Spriggs, Jennifer

  • Performing Organizations:

    University of Toledo

    Department of Civil Engineering
    2801 West Bancroft Street
    Toledo, OH  United States  43606
  • Principal Investigators:

    Chou, Eddie

  • Start Date: 20240226
  • Expected Completion Date: 20250226
  • Actual Completion Date: 0

Subject/Index Terms

Filing Info

  • Accession Number: 01906744
  • Record Type: Research project
  • Source Agency: Ohio Department of Transportation
  • Contract Numbers: 40195, 136837, 120637
  • Files: RIP, STATEDOT
  • Created Date: Feb 1 2024 1:58PM