Smart AI-Technology Employment for Crash Data Analysis

Statistics from the National Highway Traffic Safety Administration show that the United States in 2022 there were 42,795 fatalities and about 2.5 million injuries resulting from motor vehicle traffic crashes. Among many crashes, pedestrian-related car crashes hold significant importance due to their potential to cause severe injuries and loss of life, as well as their broader societal impact. These crashes underscore the vulnerability of pedestrians in collisions with vehicles. The consequences extend beyond individuals involved; the crash outcomes affect families and communities. Addressing pedestrian crashes requires a holistic approach that combines improved infrastructure, traffic regulations and enforcement, education efforts and public awareness campaigns, emergency / trauma medical care, and innovative vehicle safety technologies. The US DOT’s National Road Safety Strategy and the Safe Systems Approach reinforce the need to create more pedestrian-friendly environments and reduce the human and economic toll of these crashes, while fostering safer and more inclusive communities. In this regard, this research will take an initiative effort with crash narrative data – type of data that have not been exploited well historically to extract new insights about pedestrian-related vehicle crashes. Crash narratives include crash-related details, facilitating a deeper comprehension of each incident. By examining a collection of crash reports, one can discern recurring patterns and trends associated with specific attributes, such as particular human, roadway, vehicular, traffic control, or geographical factors. The primary objective of this research is to uncover new insights that could serve as fundamental stepping stone to foster advancements in traffic safety management. Moreover, this study aims to augment the existing knowledge base by creating an innovative methodology that harnesses Artificial Intelligence (AI) and Natural Language Processing (NLP) to efficiently delve into crash narratives, thus enhancing our level of understanding of such crashes. Methodological advancement and findings key to transportation safety will be incorporated into various educational and outreach programs at University of Nevada, Las Vegas (UNLV).

Language

• English

Project

• Status: Active
• Funding: $60767
• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Start Date: 20230601
• Expected Completion Date: 0
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Artificial intelligence; Crash analysis; Crash data; Data analysis; Pedestrian vehicle crashes; Traffic safety
• Subject Areas: Data and Information Technology; Highways; Pedestrians and Bicyclists; Safety and Human Factors;

Filing Info

• Accession Number: 01931108
• Record Type: Research project
• Source Agency: Research and Education in Promoting Safety (REPS) University Transportation Center
• Files: UTC, RIP
• Created Date: Sep 17 2024 5:38PM