AUTHOR=Xiao Yuxie , Lin Lulu , Zhou Hanchu , Tan Qian , Wang Junjie , Yang Yi , Xu Zhongzhi 

TITLE=Fatal crashes and rare events logistic regression: an exploratory empirical study

JOURNAL=Frontiers in Public Health

VOLUME=Volume 11 - 2023

YEAR=2024

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2023.1294338

DOI=10.3389/fpubh.2023.1294338

ISSN=2296-2565

ABSTRACT=Objective: Fatal road accidents are statistically rare, posing challenges for accurate estimation through the classic logit model (LM). This study seeks to validate the efficacy of a Rare Events Logit Model (RELM) in enhancing the precision of fatal crash estimations.Methods: Both LM and RELM were employed to examine the relationship between pertinent risk factors and the incidence of fatal crashes. Crashinjury datasets sourced from Hillsborough County, Florida, served as the empirical basis for evaluating the performance metrics of both LM and RELM.Results: The analysis revealed that RELM yielded more accurate predictions of fatal crashes compared to LM. Receiver Operating Characteristics (ROC) curves were constructed, and the Area Under the Curve (AUC) for each model was computed to offer a comparative performance assessment. The empirical evidence notably favored RELM over LM, as substantiated by superior AUC values.Conclusions: The study offers empirical validation that RELM is demonstrably more proficient in predicting fatal crashes than the LM, thereby recommending its application for nuanced traffic safety analytics.