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An accurate predict of traffic flow is critical for highway managers to make decisions. A wavelet neural network (WNN) model was established for forecasting traffic flow, at the same time, the prediction accuracy was improved by the training-error and test error correction. WNN model was established for a preliminary prediction of traffic flow, and then EMD-WNN model was proposed to forecast the train-error and test error. Finally, the correction of preliminary prediction values was carried out by predictive value of training-error, test-error and weighted value of the two kind of errors respectively. The contrastive experiments were carried out using the actual traffic flow data on Cheng-guan highway in Sichuan province. The results show that the prediction accuracy was improved by the WNN model with error correction, and prediction accuracy is highest when the weighted-error- correction is used.