Prediction of driver alertness levels on mountain roads using machine learning models: A naturalistic driving study in China [0.03%]
基于机器学习模型的山区驾驶警觉水平预测：一项中国的自然主义驾驶研究
Tong Liu,Deji Xie,Tangzhi Liu et al.
Tong Liu et al.
Objective: A method for evaluating driver alertness on mountain roads was developed to enhance dynamic safety monitoring in high-risk sections. An indicator system integrating human and environmental factors was establish...
Developing and evaluating the feasibility and usability of desktop simulator-based driver training for international college students seeking a US driver's license: a pilot study [0.03%]
基于桌面模拟器的驾驶员培训的开发和评估：一项针对国际大学生申请美国驾照的可行性及实用性的试点研究
Fangda Zhang,Thomas Kerwin,Christopher Robert Mitropoulos Rundus et al.
Fangda Zhang et al.
Objective: International college students seeking their first US driver's license represent a potentially risky and yet understudied road user group. They are mostly not regulated by graduated driver licensing (GDL) laws,...
Neelatphal Chanda,Ishayu Gupta,Manas Kumar Kanjilal
Neelatphal Chanda
Socio-demographic and experiential determinants of observed versus self-reported helmet use among motorcyclists in Northern Ghana: A comparative analysis [0.03%]
北吉布提摩托车乘员观测用头盔与自陈用头盔的社会人口和经历决定因素：比较分析
Benjamin Noble Adjei,Emmanuel Kweku Nakua,Charles N Mock et al.
Benjamin Noble Adjei et al.
Objective: This study assessed the socio-demographic and experiential determinants of observed and self-reported helmet use among motorcyclists in northern Ghana. ...
Analyzing the severity of motorcycle single-vehicle crashes on rural roads under diverse lighting conditions [0.03%]
不同照明条件下农村道路摩托车单方事故严重性分析
Fulu Wei,Lizu Sun,Yongqing Guo et al.
Fulu Wei et al.
Objective: Motorcycle crashes are prevalent in rural areas of China. While the specific sequence of factors contributing to single-vehicle motorcycle crashes on rural roads in different lighting conditions has not yet bee...
Causation analysis of serious road traffic accidents in Sichuan Province of China based on a hybrid HFACS-CN model [0.03%]
基于HFACS-CN混合模型的四川省严重道路交通事故因果分析研究
Shibo Zhang,Huiwen Xiong,Xin Li et al.
Shibo Zhang et al.
Objective: For the precise analysis of human factors and their interactions in serious road traffic accidents in Sichuan Province of China and to promote the prevention of such accidents. ...
Jianfeng Xi,Ting Feng,Shiyu Cao et al.
Jianfeng Xi et al.
Objective: With the widespread application of photovoltaic technology in transportation infrastructure, the potential threat to driving safety posed by glare generated by roadside distributed photovoltaic systems has beco...
Nae Y Won,Sarah Bird,Julia Wrobel et al.
Nae Y Won et al.
Objective: To assess driving performance after consuming edible cannabis using a driving simulator, examining frequency of use, THC dose, and rural versus urban settings. ...
Analysis of injury severity of single-vehicle and two-vehicle crashes involving lightweight vehicles (K-car) in Japan: A random parameters approach with heterogeneity in means [0.03%]
日本轻型汽车(K-car)单车及双车型交通事故伤害严重度分析：均值异质性随机参数模型方法
Lin Wang,Jaeyoung Jay Lee,Junjie Hu et al.
Lin Wang et al.
Objectives: This study investigates the factors influencing injury severity in crashes involving lightweight vehicles (K-cars) in Japan, addressing safety concerns arising from their structural vulnerability. It aims to o...
Investigating determinants of injury severity in motorcycle-vehicle crashes using interpretable machine learning models [0.03%]
基于可解释机器学习模型的机动车与摩托车碰撞事故伤者损伤严重程度决定因素研究
Shuwu Wei,Guopeng Zhang,Lei Li et al.
Shuwu Wei et al.
Objective: Due to inherent vulnerability, motorcyclists sustain more severe injuries in motorcycle-vehicle crashes. Although previous studies have analyzed diverse factors affecting injury severity of crashes involving a ...