Leveraging Big Data and Artificial Intelligence to Streamline Safety Data Analyses

The objective of this research is to advance the use of AI and ML in analyzing BD and unconventional data and assessing their effectiveness to support safe system and modal priority decision-making as well as performance tracking. The resultant algorithms are expected to improve and optimize analyses using existing data and data-driven safety analysis tools developed based on conventional statistical modeling (see, for example, NCHRP Research Report 955: Guide for Quantitative Approaches to Systemic Safety Analysis). Note: Assessing the effectiveness of BD and unconventional data might include, for example, determining biases in the data or identifying data that do not represent an entire population. The research will also (a) identify potential data sources, (b) identify or develop the requisite data preparation and extraction algorithms for use in safety analysis, and (c) document each source’s coverage, frequency of collection, granularity, accessibility to practitioners, and cost. These sources shall include but not be limited to video data, telematics, LiDAR, satellite, aerial imagery, weather, land use, location-based services data, crowd-sourced data, and demographic and census data. This data will allow the potential for lower-cost and more frequent generation of, among others: key fatality and injury prediction risk maps; road feature mapping; star ratings and other safety analyses for pedestrians, cyclists, motorcyclists, micro-mobility services, and vehicle occupants; identification of data for safety analyses and associated tools; and the development of safety plans that can be used for funding submissions and in prioritizing investments across the local and state road networks. Finally, this research will develop guidance for managing data using a format that can be accessed by various tools. This guidance should be tested through pilot projects to allow for appropriate adjustment and greater understanding. The development of guidance will enhance implementation and provide necessary information on the use of this data in safety systems and in determining modal priority needs. Results of this research could be included in national-level resources such as the AASHTO Highway Safety Manual and other tools that support data-driven safety analysis.

Language

  • English

Project

  • 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: 20210803
  • 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