Investigating the contributors to hit-and-run crashes using gradient boosting decision trees.

Investigating the contributors to hit-and-run crashes using gradient boosting decision trees.

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 model is trained...

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Bibliographic Details
Main Authors: Baorui Han, Haibo Huang, Gen Li, Chenming Jiang, Zhen Yang, Zhenjun Zhu
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0314939
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