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

Yuxie Xiao; Lulu Lin; Hanchu Zhou; Qian Tan; Junjie Wang; Yi Yang; Zhongzhi Xu

doi:10.3389/fpubh.2023.1294338

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

Front Public Health. 2024 Jan 5:11:1294338. doi: 10.3389/fpubh.2023.1294338. eCollection 2023.

Authors

Yuxie Xiao^{1

2}, Lulu Lin¹, Hanchu Zhou³, Qian Tan², Junjie Wang⁴, Yi Yang^{5

6}, Zhongzhi Xu¹

Affiliations

¹ School of Public Health, Sun Yat-sen University, Guangzhou, China.
² Engineering Consulting Department, Changsha Planning and Design Institute Co., Ltd., Changsha, China.
³ School of Traffic and Transportation Engineering, Central South University, Changsha, China.
⁴ Institute of Transportation System Science and Engineering, Beijing Jiaotong University, Beijing, China.
⁵ School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China.
⁶ National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Chengdu, China.

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 logistic 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. Crash-injury 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 characteristic (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.

Conclusion: 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.

Keywords: binary classification; fatal crashes; logit model; rare events; traffic safety.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Accidents, Traffic*
Florida / epidemiology
Logistic Models
ROC Curve
Risk Factors

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Sun Yat-sen University Basic Start-up Funding (51000-12230014).