In order to overcome the problem of low fitting between traffic uncertainty prediction results and actual values in existing research methods, a traffic flow uncertainty prediction method based on K-nearest neighbor algorithm is proposed. The original database, classification center database, k-nearest neighbor database and intermediate search database are used to construct the database needed in the prediction process. Based on the database, multivariate linear regression is used to assign weights to state variables, and k-nearest neighbor algorithm and Kalman filter are used to update the weights to adapt to the uncertainties of traffic flow until the predicted values are obtained, and the uncertainties of traffic flow are predicted. The experimental results show that the maximum average absolute error and average relative error of the proposed method are 0.018 and 0.02, respectively. Compared with the traditional method, the proposed method has higher overall prediction accuracy, higher fitting degree, and is feasible.

中文翻译：

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基于K最近邻算法的交通流量不确定性预测方法

为了解决现有研究方法中交通不确定性预测结果与实际值之间拟合度低的问题，提出了一种基于K近邻算法的交通流不确定性预测方法。原始数据库，分类中心数据库，k最近邻数据库和中间搜索数据库用于构建预测过程所需的数据库。基于该数据库，使用多元线性回归为状态变量分配权重，并使用k最近邻算法和卡尔曼滤波器更新权重以适应交通流量的不确定性，直到获得预测值和不确定性为止交通流量的预测。实验结果表明，该方法的最大平均绝对误差和平均相对误差分别为0.018和0.02。与传统方法相比，该方法具有较高的总体预测精度，较高的拟合度，是可行的。