Development of Railroad Trespassing Database Using Artificial Intelligence

Details

• Creators:
  Zaman, Asim ; Huang, Zhe ; Li, Weitian ; Qin, Huixiong ; Kang, Di ; Liu, Xiang
• Corporate Creators:
  Rutgers University
• Corporate Contributors:
  United States. Department of Transportation. Federal Railroad Administration. Office of Railroad Policy and Development ; United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development and Technology
• Subject/TRT Terms:
  • Railroad grade crossings
  • Right of way (Traffic)
  • Artificial intelligence
  • Railroad safety
  • Human factors
  • Trespassers
  • Trespassing
• Publication/ Report Number:
  DOT/FRA/ORD-24/09
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  May 2020 - September 2023
• Contracting Officer:
  Jacobini, Francesco Bedini
• Corporate Publisher:
  United States. Department of Transportation. Federal Railroad Administration
• Abstract:
  The Federal Railroad Administration (FRA) sponsored a research team from Rutgers University to develop a proof-of-concept Trespassing Database using Artificial Intelligence (AI) technology to automatically process large volumes of live or recorded video data. The team used the Rutgers AI algorithm to analyze over 27,000 hours of live video data and 1,176 hours of recorded video data from rights-of-way and grade crossings at 11 locations in 6 states. The AI algorithm collected trespassing-related data, including traffic, rail signal activations, train events, and trespass events. Trespass event data were automatically collected for each trespasser, including date, time, type (e.g., person, car, truck, bus, motorcycle), weather, trespasser's path, and a video clip. The team manually validated all trespass event detection results to ensure that accurate data was included in the database. Over 29,000 trespass events were detected by the AI algorithm across all studied locations in this research.
• Format:
  PDF
• Funding:
  693JJ620C000009
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/73444/dot_73444_DS1.pdf
• File Type:
  [PDF - 8.00 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; Federal Railroad Administration
• Main Document Checksum:
  urn:sha-512:d6fcd7c9537fccb338118a505c5b042b8abc4e90b7072b2bc3d762627e24a7b08099e09306273fed0d11df1f372a405741a6ede17d10478893e451866fbb99bf