School of Public Health, Sun Yat-sen University, Guangzhou, China.
Engineering Consulting Department, Changsha Planning and Design Institute Co., Ltd., Changsha, China.
Front Public Health. 2024 Jan 5;11:1294338. doi: 10.3389/fpubh.2023.1294338. eCollection 2023.
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.
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.
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.
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.
从统计学角度来看，致命道路事故较为罕见，这给经典逻辑模型（LM）的精确估计带来了挑战。本研究旨在验证罕见事件逻辑模型（RELM）在提高致命碰撞事故估计精度方面的有效性。
本研究采用 LM 和 RELM 来检验相关风险因素与致命碰撞事故发生率之间的关系。佛罗里达州希尔斯伯勒县的碰撞伤害数据集被用作评估 LM 和 RELM 性能指标的实证基础。
分析表明，RELM 相较于 LM 能更准确地预测致命碰撞事故。构建了受试者工作特征（ROC）曲线，并计算了每个模型的曲线下面积（AUC），以提供比较性能评估。实证证据明显倾向于 RELM 优于 LM，这体现在 AUC 值更高上。
本研究提供了经验验证，表明 RELM 在预测致命碰撞事故方面明显优于 LM，因此推荐在细致的交通安全分析中应用 RELM。
