关键词: 数据缺失, 交通流, 小波降噪, 卡尔曼平滑滤波, 短时预测

Abstract: In view of missing data issue of traffic detection, this paper proposed a new short-term traffic flow composite forecasting model. The model adopted reconstruction method to solve the missing data problem, and used improved Kalman smoothing to implement short-term traffic flow forecasting. The model resolved the defeats of traditional forecasting methods which cannot deal with the missing data, and also can attain a high forecasting precision. Through the validation of Shenzhen data and being compared with the traditional methods, it has been proved that the new method has high forecasting precision, the forecasting result can maintain at 88% or more, and the model also has good practicality.

Key words: data missing, traffic flow, wavelet de-noising, Kalman smooth filtering, short-term forecasting