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山区双车道公路货车移动遮断小客车跟驰风险预测模型

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选取典型山区双车道公路弯道和直道为研究对象，基于无人机航拍视频提取的交通轨迹数据，通过轻度提升机算法构建了货车移动遮断下小客车跟驰风险预测模型，采用支持向量机、随机森林验证了模型有效性，分析了模型关键特征参数风险作用机制。实验结果表明:基于轻度提升机算法的风险预测模型准确率达96。9%，具有优越性，速度差、跟驰间距是模型关键特征参数，直道上单因子重要度更大;相比弯道，直道路段危险驾驶行为突出，大幅横向偏移等不稳定跟驰特征明显;由模型解释器结果可知，当速度差小于0。5 m/s、跟驰间距大于40 m时，是较为安全的跟驰状态。
Risk prediction model of passenger car following behavior under truck movement interruption of two-lane highway in mountainous area
Taking the typical mountainous two-lane highway bend and straight road as the research object,based on traffic trajectory data extracted by UAV aerial video,the risk prediction model of passenger car following under the movement interruption of truck was constructed by the light gradient boosting machine algorithm(LGBM).The support vector machine(SVM)and random forest machine(RF)were used to verify the validity of the model,and the risk mechanism of the key characteristic parameters of the model was analyzed.The experimental results show that the accuracy of the risk prediction model based on the LGBM algorithm is 96.9%,which is superior.The speed difference and the following distance are the key characteristic parameters of the model,and the single factor importance on the straight road is greater.Compared with the curve,the dangerous driving behavior of straight road section is prominent,and the unstable following characteristics such as large lateral offset are obvious;the results of the model interpreter show that when the speed difference is less than 0.5 m/s and the car-following distance is greater than 40 m,it is a safe car-following state.

traffic and transportation safety engineeringrisk prediction of car-followingLGBM algorithmtruck movement interruptionmountain two-lane highway

戢晓峰、徐迎豪、普永明、郝京京、覃文文

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昆明理工大学 交通工程学院,昆明 650504

云南省现代物流工程研究中心,昆明 650604

交通运输安全工程 跟驰风险预测 轻度提升机算法 货车移动遮断 山区双车道公路

国家自然科学基金云南省交通厅科技创新及示范项目云南省交通厅科技创新及示范项目云南省基础研究计划青年项目

520620242021-90-32022-27二202101AU070166

2024

10.13229/j.cnki.jdxbgxb.20220744

吉林大学学报(工学版)
吉林大学

吉林大学学报(工学版)

CSTPCD北大核心
影响因子：0.792
ISSN：1671-5497
年,卷(期)：2024.54(5)
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