Relative importance of hit-and-run variables.

<div><p>A classification prediction model is established based on a nonlinear method—Gradient Boosting Decision Tree (GBDT) to investigate the factors contributing to a perpetrator’s escape behavior in hit-and-run crashes. Given the U.S. Crash Report Sampling System (CRSS) dataset, the m...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Baorui Han (20501556) (author)
مؤلفون آخرون:	Haibo Huang (1555780) (author), Gen Li (743590) (author), Chenming Jiang (3774226) (author), Zhen Yang (107412) (author), Zhenjun Zhu (7364723) (author)
منشور في:	2025
الموضوعات:	Biotechnology Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified mine hidden information lowest negative log accident safety analysis run incident prevention crash type </ div >< p logistic regression ). run crashes compared classification prediction model run crashes logistic regression trafficway </ 803 ). regression tree various variables relative importance rank 4th random forest previous studies practical applications perpetrator ’ lift value joint effect influencing variables highest auc factors contributing established based escape behavior efficient model 096 ),
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Relative importance of hit-and-run variables.

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