驾驶员因素与交通事故率的关联性

    Correlation Between Driver's Factors and Traffic Accident Rate

    • 摘要: 驾驶员因素是导致交通事故发生的主要因素之一.综合考虑年龄、性别、累计驾驶时间、生理状况等多个驾驶员特征参数,通过问卷调查的方式,利用BP神经网络分析技术,建立具有不同隐含层、神经元个数、作用函数的神经网络结构,对驾驶员因素与交通事故二者之间的内在关联性进行研究,确定最优的神经网络结构对事故率进行预测.结果表明,采用BP神经网络对驾驶员交通事故率预测可行,同时明确了易发交通事故的驾驶员子组.

       

      Abstract: Driver's factors are very important for the traffic safety. Questionnaire and BP neural network are used, the neural network structure with different hidden layer, neuron number and transfer function is established considering many parameters, such as age, gender, accumulative driving time, physiological conditions, etc. The optimal neural network structure is obtained to predict the traffic accident rate. The results show that the neural network is available for the prediction of accident rate and the driver subgroups with accident proneness are identified.

       

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