The current navigation software has obvious inaccurate speed assessment when facing some serious traffic congestion, and cannot accurately predict the duration of the traffic congestion. Therefore, we propose a traffic congestion prediction model to accu- rately predict the congestion time in the face of most congestion situations through the prediction of speed. Regarding the speed pre- diction model, we select high-similarity samples based on the KNN algorithm. The prediction speed model is divided into two main models, KNN-VA and KNN-RBF, and we use an integrated learning method to fuse these two models to obtain more accurate aver- age speed prediction. Then, the congestion time can be predicted. In order to determine the congestion time, we use the RBF speed prediction method and the sampling method in a fixed area to verify. The results show that the model has high reliability for conges- tion time prediction.