Towards Building a Foundation AI Model for Railway Safety

Details

• Creators:
  Papalexakis, Evangelos ; Chen, Jia ; Dong, Yue
• Corporate Creators:
  University Transportation Center for Railway Safety (UTCRS). University of California Riverside (UCR)
• Corporate Contributors:
  United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  Artificial Intelligence Data Mining Data Science Railroad Rails Safety Analysis
• Publication/ Report Number:
  UTCRS-UCR-O6CY24
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  June 1, 2024 – August 31, 2025
• Corporate Publisher:
  University Transportation Center for Railway Safety (UTCRS) Tier-1 University Transportation Center (UTC)
• Abstract:
  Large language models (often also termed foundation models) are revolutionizing multiple aspects of every-day life and work, with the use of artificial intelligence (AI) agents like ChatGPT being now commonplace and transforming life, work, and scientific discovery as we know it. In this project, we harness the power of such foundation models in order to transform railway safety. Specifically, we build proof-of-concept prototypes that demonstrate the viability of foundation models for railway safety question-answer generation and accident report-news article alignment. Furthermore, we develop algorithms for the improvement of performance of large language models, including hallucination detection foundation and model denoising.
• Format:
  PDF
• Funding:
  69A3552348340
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:f725005ee72d18ab179032fcd365a2c1a16443d6fd2e1fe7bbd5ff024612b6ce8bb35a6844b34d8fda6f561239c702a4226cff23932ca4a85bc1467361f8b415
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/88689/dot_88689_DS1.pdf
• File Type:
  [PDF - 1.44 MB ]