Behavior-Based Predictive Safety Analytics Phase II

Details

• Creators:
  Miller, Andrew ; Sarkar, Abhijit ; McDonald, Tony ; Ghanipoor Machiani, Sahar ; Jahangiri, Arash
• Corporate Creators:
  Safety through Disruption (Safe-D) University Transportation Center (UTC) ; Virginia Tech Transportation Institute
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Crash injuries
  • Near crashes
  • Crashes
  • Automatic data collection systems
  • Behavior
  • Crash data
  • Crash risk forecasting
  • Demographics
  • Drivers
  • Human characteristics
  More +
• Publication/ Report Number:
  04-114
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  Safety through Disruption (Safe-D) University Transportation Center (UTC)
• Abstract:
  This project addressed the emerging field of behavior-based predictive safety analytics, focusing on the prediction of road crash involvement based on individual driver behavior characteristics. This has a range of applications in the areas of fleet safety management and insurance, but may also be used to evaluate the potential safety benefits of an automated driving system. This project continued work from a pilot study that created a proof-of-concept demonstration on how crash involvement may be predicted on the basis of individual driver behavior utilizing naturalistic data from the Second Strategic Highway Research Program. The current project largely focuses on understanding and identifying the risks from a driver based on their driving behaviors, personal characteristics, and environmental influences. This project analyzed large scale continuous naturalistic data as well as event data to study the role of different driving behaviors in the buildup of risk related to a safety-critical event or crash. This research can be used structure the development of real-time crash risk that accounts for those identified driver behaviors to be evaluated across the contextualized information on a roadway.
• Format:
  PDF
• Funding:
  69A3551747115/04-114
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/73306/dot_73306_DS1.pdf
• File Type:
  [PDF - 1.68 MB]
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:b147b8b5f3df85ed08d35423c05e73ff1a08898e0f94b378ade37bb5d36a112df29e011efa6d74b8e3403e0136a16e7fb941512934d7841c888fc3c5fff34895