Mining and Learning from Railway Safety Data with Graphs and Tensors

Railway systems are very complex pieces of cyberinfrastructure, interfacing with a number of transportation agents and other pieces of cyberinfrastructure. For instance, a railway crossing includes interactions between the railway system and a traffic intersection. Such a rich ecosystem of interactions among heterogeneous agents poses fascinating research challenges in modeling railway systems with data and conducting data-driven railway crossing safety assessment. In this project, the research team proposes to leverage and extend powerful tensor and graph mining methods which can extract “needles in the haystack” within the abundance of collected data and produce actionable insights to stakeholders in order to better understand emerging accident patterns from historical data, identify underlying similarities in such patterns, towards ultimately reducing the number of accidents.

• Record URL:
  https://www.utrgv.edu/railwaysafety/_files/documents/research/exhibit-d_utcrs_ucr_mining_and_learning_from_railway_safety_data_2023.pdf
• Supplemental Notes:
  • Partners for this project are the University of Nebraska Lincoln, University of South Carolina, and the University of Texas Rio Grande Valley.

Language

• English

Project

• Status: Active
• Funding: $150000
• Contract Numbers:

  69A3552348340

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  University of California, Riverside

  1084 Columbia Ave.
  Riverside, CA  United States  92507
• Managing Organizations:

  University of California, Riverside

  1084 Columbia Ave.
  Riverside, CA  United States  92507
• Project Managers:

  Stearns, Amy

• Performing Organizations:

  University of California, Riverside

  1084 Columbia Ave.
  Riverside, CA  United States  92507
• Principal Investigators:

  Chen, Jia

  Papelexakis, Evangelos (Vagelis)

• Start Date: 20230601
• Expected Completion Date: 20240831
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Data mining; Railroad crashes; Railroad safety
• Subject Areas: Data and Information Technology; Railroads; Safety and Human Factors;

Filing Info

• Accession Number: 01898080
• Record Type: Research project
• Source Agency: University Transportation Center for Railway Safety
• Contract Numbers: 69A3552348340
• Files: UTC, RIP
• Created Date: Oct 31 2023 9:33PM