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Beyond the conventional: Exploring pedestrian safety on interstates with Bayesian and machine learning models.

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  • 1Department of Civil & Environmental Engineering, The University of Tennessee, Knoxville, United States.

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Pedestrian freeway crashes are more severe when pedestrians are impaired, crossing the road, or on unlit roads. Improving lighting and using technology can enhance freeway pedestrian safety.

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Area of Science:

  • Traffic Safety
  • Transportation Engineering
  • Public Health

Background:

  • Federal laws prohibit pedestrians on freeways, yet 14-17% of US pedestrian crashes occur on interstates.
  • Examining interstate pedestrian crashes through a safe systems approach is crucial for mitigating risks.
  • This study focuses on pedestrian crash injury severity on North Carolina freeways.

Purpose of the Study:

  • Investigate correlates of pedestrian crash injury severity on interstates.
  • Analyze pedestrian actions, roadway conditions, and vehicle types in freeway crashes.
  • Utilize comprehensive crash data from 2007-2022.

Main Methods:

  • Employed frequentist and Bayesian binary logit models.
  • Utilized the Random Forest machine learning algorithm for robust estimates.
  • Analyzed 882 pedestrian crash observations on freeways.

Main Results:

  • Pedestrian crashes are more frequent on rural (47%) than urban (40%) freeways.
  • Higher risk of severe pedestrian injuries associated with standing on road (OR=2.40), crossing freeway (OR=1.645), alcohol impairment (OR=2.51), and dark unlit segments (OR=2.00).

Conclusions:

  • A multi-tiered approach is needed for freeway pedestrian safety, aligning with the Safe Systems Pyramid.
  • Strategies include enhancing roadway lighting and implementing variable speed limits at night.
  • Transportation technologies to alert drivers to pedestrians can improve safety.