Prediction of driver alertness levels on mountain roads using machine learning models: A naturalistic driving study in China

Driver alertness levels were effectively quantified by integrating human and environmental factors. TabPFN model was found to be more effective than logistic regression scoring and XGBoost, making it the preferred approach for monitoring driver alertness on mountain roads.

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Publication Details

Journal
Traffic Injury Prevention
Published
2025-11-20
DOI
https://doi.org/10.1080/15389588.2025.2577155
Primary Topic
Sleep and Work-Related Fatigue
Type
article
Field-Weighted Citation Impact
0.00
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article

Prediction of driver alertness levels on mountain roads using machine learning models: A naturalistic driving study in China

Tong Liu, Tangzhi Liu, Jue Shan, Donghua Xie et al.
Traffic Injury Prevention
Sleep and Work-Related Fatigue
article

Prediction of driver alertness levels on mountain roads using machine learning models: A naturalistic driving study in China

Tong Liu, Tangzhi Liu, Jue Shan, Donghua Xie, Chun Pan Tang
article en

Abstract

Driver alertness levels were effectively quantified by integrating human and environmental factors. TabPFN model was found to be more effective than logistic regression scoring and XGBoost, making it the preferred approach for monitoring driver alertness on mountain roads.

Traffic Injury Prevention
Chongqing Jiaotong University (CN)
Openalex Percentile: Top 62%
Sleep and Work-Related Fatigue
DOISourceOpenAlex
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Prediction of driver alertness levels on mountain roads using machine learning models: A naturalistic driving study in China — Tong Liu, Tangzhi Liu, et al. · Traffic Injury Prevention (2025) | TGRS Research Map | TGRS