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Fatal crashes and rare events logistic regression: an exploratory empirical study.
Xiao, Yuxie; Lin, Lulu; Zhou, Hanchu; Tan, Qian; Wang, Junjie; Yang, Yi; Xu, Zhongzhi.
Affiliation
  • Xiao Y; School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Lin L; Engineering Consulting Department, Changsha Planning and Design Institute Co., Ltd., Changsha, China.
  • Zhou H; School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Tan Q; School of Traffic and Transportation Engineering, Central South University, Changsha, China.
  • Wang J; Engineering Consulting Department, Changsha Planning and Design Institute Co., Ltd., Changsha, China.
  • Yang Y; Institute of Transportation System Science and Engineering, Beijing Jiaotong University, Beijing, China.
  • Xu Z; School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China.
Front Public Health ; 11: 1294338, 2023.
Article in En | MEDLINE | ID: mdl-38249366
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.
Subject(s)
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Accidents, Traffic Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies Country/Region as subject: America do norte Language: En Journal: Front Public Health Year: 2023 Document type: Article Affiliation country: China Country of publication: Switzerland

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Accidents, Traffic Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies Country/Region as subject: America do norte Language: En Journal: Front Public Health Year: 2023 Document type: Article Affiliation country: China Country of publication: Switzerland