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Area of Science:

  • Environmental Science
  • Transportation Engineering
  • Data Science

Background:

  • Existing transportation datasets often miss non-accidental disruptions.
  • Natural hazards pose significant risks to critical infrastructure like railroads.
  • Understanding hazard exposure is key for infrastructure resilience.

Purpose of the Study:

  • To create a comprehensive dataset linking natural hazards to the U.S. railroad network.
  • To provide a baseline for assessing railroad infrastructure vulnerability to climate change.
  • To support research and planning for transportation resilience.

Main Methods:

  • Spatial analysis of federal agency data on natural hazards (flooding, excessive heat, landslides).
  • Integration of multiple federal databases to map hazard events to railroad segments.
  • Verification using the Federal Railroad Administration (FRA) accident records for spatial-temporal accuracy.

Main Results:

  • A new national dataset assigning historical natural hazard events to U.S. railroad segments (2000-2024).
  • Dataset demonstrates spatial-temporal accuracy, with up to 50% of hazard-linked accidents coinciding with recorded hazard events.
  • Provides segment-level hazard exposure data across the continental U.S.

Conclusions:

  • The dataset is a valuable tool for understanding railroad infrastructure vulnerability.
  • Facilitates academic research, planning, and climate resilience assessments for transportation.
  • Highlights the need to consider natural hazard impacts beyond reportable accidents.