The location prediction of mobile objects on the road network is the basis of many location-related services. At present, the method for predicting the position of a moving object does not fully consider the road congestion information which mining from the trajectory data, and the congestion state has a great influence on the position update of the moving object. A meta-congestion-pattern mining (MCPM) method is proposed. In the offline mining stage, local congestion patterns are mining from the tightly-concentrated frequent paths (meta-paths) of the historical trajectories, and the movement patterns are modeled. A mean-based clustering algorithm is used to solve the data sparsity problem. The online forecasting stage predicts the probability based on the mining congestion model and the motion pattern. Finally, the validity of the algorithm is obtained by theoretical analysis and experimental verification. Compared with the WN method under the same conditions, the average prediction accuracy is improved by nearly 20%, and the prediction time is shortened by an average of nearly 50%.