公路小半径曲线路段驾驶人焦虑水平模型

Anxiety level model of drivers on sharp circular curve road

为提高双车道公路小半径曲线路段(SCCR)的交通安全监测水平,利用量表测试与驾驶模拟试验,实现驾驶人弯道焦虑水平的量化评测,定量刻画驾驶人弯道焦虑水平与行车安全特征量之间的关系;通过统计分析焦虑驾驶行为,运用Spearman分析法,筛选影响焦虑水平的关键行车安全特征量;综合道路线形条件、驾驶人个体特征和驾驶操作行为特性等行车安全特征因子,采用径向基神经网络(RBFNN)建立多因素驾驶人焦虑水平预测模型。结果表明:弯道焦虑水平与驾龄、年龄呈显著负相关,与车速、侧向偏移量、转角变异系数以及曲线半径之间存在着较为显著的负相关性(显著性概率p值<0.01);通过验证,基于RBFNN的驾驶人焦虑水平预测模型判别精度可达73.7%;转角变异系数、年龄、驾龄是影响驾驶人焦虑水平的重要因素,其重要度依次为100%、93.3%、90.7%。研究结果可为双车道公路SCCR驾驶焦虑水平监测、交通安全维护等方面提供理论支撑。

In order to improve the traffic safety monitoring level for SCCR of a two-lane highway,a scale measurement and driving simulation experiments were used to quantitatively evaluate the driver's anxiety level on curves and describe the relationship between the driver's anxiety level and the driving safety characteristics.Through the statistics of anxious driving behaviors,spearman analysis was used to screen the key driving safety characteristics that affect the anxiety level.A multi-factor driver anxiety level prediction model was established by using a radial basis function neural network(RBFNN),which integrates some driving safety characteristic factors consisting of road alignment conditions,individual characteristics of drivers and driving behavior characteristics.The results show that the anxiety level is significantly negatively correlated with driving age and driver's age,and significantly negatively correlated with vehicle speeds,lateral offset,corner variation coefficient and curve radius(p<0.01).Through verification,the prediction model of driver's anxiety level based on radial basis function neural network has a high discrimination accuracy,up to 73.7%.The angle variation coefficient,age and driving age are the important factors affecting the driver's anxiety level,and their importance is 100%,93.3%and 90.7%,respectively.The research results can provide theoretical support for monitoring driving anxiety levels and the maintenance of traffic safety in SCCR of two-lane highways.