There are a lot of noise data and missing data in the traffic data of expressway network,and the data integrity is not high,leading to the decline of prediction accuracy.A real-time prediction method for short-term traffic flow parameters of expressway network based on multi-source data fusion is proposed.The wavelet analysis threshold method is used to denoise the traffic data of the expressway network.Based on the least squares support vector machine,the combined threshold filling method is used to fill in the missing data in the traffic data sequence to improve the integrity of the traffic data.The short-term traffic flow parameter prediction model is established by combining wavelet neural network and genetic algorithm.The traffic flow parameters collected by multi-source detectors are processed by genetic wavelet neural network.The traffic flow parameters of multiple detectors are fused by the least squares dynamic weighted fusion algorithm.The traffic flow parameters are input into the prediction model to obtain the real-time prediction results of short-term traffic flow parameters of expressway network.The experimental results show that there is no missing data in the traffic data series processed by the proposed method,the data integrity is high,and the predicted results are close to the actual vehicle flow change curve,with high prediction accuracy,which can be widely used in the field of traffic flow prediction.
维普
万方数据