Nighttime Pedestrian Safety in Different Communities: Application of Artificial Intelligence Techniques

Usman, Sheikh M.; Khattak, Asad J.; Patwary, A. Latif

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Nighttime Pedestrian Safety in Different Communities: Application of Artificial Intelligence Techniques

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

File Language:

English

Details

Creators:

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

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

Artificial Intelligence Crash Risk Forecasting Fatalities Heterogeneity Injury Severity Night Visibility Pedestrian Safety Predictive Models Traffic Lanes
Resource Type:

Manuscript
Geographical Coverage:

United States ; North Carolina
Corporate Publisher:

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

Pedestrian safety is a growing concern in the United States transportation sector, with around 7500 pedestrian crash fatalities reported in the US in 2021. Pedestrians are at an even higher risk of crashes at night due to limited visibility and alcohol impairment of the drivers or pedestrians. The United States Department of Transportation (USDOT) has integrated six unique indicators (Economy, Health, Language Proficiency, Resilience, Environmental, and Transportation Access) at the census tract level to promote safety and achieve its Vision Zero goal. This study integrates these six indicators with pedestrian crash data from 2016 to 2019 in North Carolina. The pedestrian crash data are extracted from police reports using the Pedestrian and Bicyclist Crash Analysis Tool, which provides high-quality, detailed crash-type descriptors, resulting in a unique and comprehensive pedestrian crash database. Methods: The study applies rigorous methods for analysis, including the inference-based ordered logit model, to quantify key correlates of nighttime pedestrian crash injury severity in communities. To improve forecasting of pedestrian crashes and the resulting injury severity for planning purposes, an Artificial Intelligence (AI) based heterogeneous ensemble method, “Stacking,” is applied with an Ordered Logit model and machine-learning techniques, Gradient Boosting, Decision Tree, and Random Forest as the base learners.
Format:

PDF
Collection(s):

US Transportation Collection
Main Document Checksum:

urn:sha-512:934c4012b173fe3620b0985e3fb0e54c35692dff6e4983803a2a3752178e7997433aaa05583a52321abf115a384328ee2c059ec1e7dd5b22bcfd3ea2b7dc3154
Download URL:

https://rosap.ntl.bts.gov/view/dot/86905/dot_86905_DS1.pdf
File Type:

[PDF - 1.64 MB ]

File Language:

English

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