Leveraging Big Data and Artificial Intelligence to Streamline Safety Data Analyses

The objective of this research is to advance the use of artificial intelligence (e.g. machine learning) and big data in supporting safe system and modal priority decision making and performance tracking. This research will develop the algorithms and necessary machine learning mechanisms to accelerate the intelligent coding of road attributes that are used in existing data-driven tools including usRAP and IHSDM. The research will also identify potential data sources that can be used for intelligent data collection including video data, telematics, LiDAR, satellite and aerial imagery, as well as document each source’s coverage and frequency of collection. This attribute data will then allow for lower-cost and more frequent generation of key fatality and injury prediction risk maps; road feature mapping; star ratings for pedestrians, cyclists, motorcyclists and vehicle occupants; source data for IHSDM analyses and the development of safety plans that can be used for funding submissions and in prioritizing investments across the local and state road networks.


  • English


  • Status: Proposed
  • Funding: $650000
  • Contract Numbers:

    Project 17-100

  • Sponsor Organizations:

    National Cooperative Highway Research Program

    Transportation Research Board
    500 Fifth Street, NW
    Washington, DC  United States  20001

    American Association of State Highway and Transportation Officials (AASHTO)

    444 North Capitol Street, NW
    Washington, DC  United States  20001

    Federal Highway Administration

    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590
  • Project Managers:

    Harrigan, Edward

  • Start Date: 20210324
  • Expected Completion Date: 0
  • Actual Completion Date: 0

Subject/Index Terms

Filing Info

  • Accession Number: 01767991
  • Record Type: Research project
  • Source Agency: Transportation Research Board
  • Contract Numbers: Project 17-100
  • Files: TRB, RiP
  • Created Date: Mar 22 2021 3:13PM