Beyond the Conventional: Exploring Pedestrian Safety on Interstates with Bayesian and Machine Learning Models

Usman, Sheikh M.; Khattak, Asad J.

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Beyond the Conventional: Exploring Pedestrian Safety on Interstates with Bayesian and Machine Learning Models

2025-09-01
By Usman, Sheikh M. ; Khattak, Asad J.

File Language:

English

Details

Creators:

Usman, Sheikh M. ; Khattak, Asad J.
Corporate Creators:

University of Tennessee, Knoxville. Department of Civil and Environmental Engineering
Subject/TRT Terms:

Access Roads Crash Characteristics Crash Injuries Freeways Injury Severity Machine Learning Pedestrian Safety
Resource Type:

Manuscript
Geographical Coverage:

United States ; North Carolina
Corporate Publisher:

University of Tennessee, Knoxville. Department of Civil and Environmetal Engineering
Abstract:

Despite being prohibited from walking on freeways per federal laws, 14% to 17% of all pedestrian crashes in the United States happen on the interstates. Examining these crashes within the context of the safe systems approach is essential, with an emphasis on mitigating safety risks for all road users. This study investigates the correlates of pedestrian crash injury severity on interstates in North Carolina, focusing on pedestrian actions, roadway conditions, and the type of vehicles involved in the crashes. The study utilizes police-reported pedestrian crash data from 2007 to 2022, coded by the Pedestrian and Bicyclist Crash Analysis Tool (PBCAT), providing unique and comprehensive crash descriptors. The analysis considers 882 pedestrian crash observations on freeways. The dependent variable, pedestrian injury severity, is categorized into distinct binary outcomes: fatal and severe injuries versus minor injuries. Methods: The study applies frequentist and Bayesian binary logit models with various prior specifications and a robust machine learning algorithm, Random Forest, for their ability to provide reliable estimates even with a limited sample size.
Format:

PDF
Collection(s):

US Transportation Collection
Main Document Checksum:

urn:sha-512:7f1daeb3e686ec05e7bf5d0295a188378a6542029fe6c6d386818640086d3532716222f3cdec516161ec2c5a6e8f709573e35c97ee9bd7186cccefde5b7ed145
Download URL:

https://rosap.ntl.bts.gov/view/dot/86906/dot_86906_DS1.pdf
File Type:

[PDF - 945.26 KB ]

File Language:

English

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