Citation

McCarty D, Kim HW. PLoS One 2024; 19(1): e0296663.

Copyright

(Copyright © 2024, Public Library of Science)

DOI

10.1371/journal.pone.0296663

PMID

38252612

PMCID

PMC10802962

Abstract

Human behavior is a dominant factor in road accidents, contributing to more than 70% of such incidents. However, gathering detailed data on individual drivers' behavior is a significant challenge in the field of road safety. As a result, researchers often narrow the scope of their studies thus limiting the generalizability of their findings. Our study aims to address this issue by identifying demographic-related variables and their indirect effects on road accident frequency. The theoretical basis is set through existing literature linking demographics to risky driving behavior and through the concept of "close to home" effect, finding that the upwards of 62% of accidents happen within 11km of a driver's home. Using regression-based machine learning models, our study, looking at England, UK, explores the theoretical linkages between demographics of an area and road accident frequency, finding that census data is able to explain over 28% of the variance in road accident rates per capita. While not replacing more in-depth research on driver behavior, this research validates trends found in the literature through the use of widely available data with the use of novel methods. The results of this study support the use of demographic data from the national census that is obtainable at a large spatial and temporal scale to estimate road accident risks; additionally, it demonstrates a methodology to further explore potential indirect relationships and proxies between behaviors and road accident risk.

Language: en