Achieving complete and accurate traffic data as input is crucial for most intelligent transportation systems. However, due to hardware or software malfunction, traffic data is inevitably faced with missing and noise problems. Most of the existing representationbased traffic data recovery methods adopt sparse representation theory, which well models the local association properties of traffic data, but ignores their global correlation. To overcome this shortcoming, a robust low-rank representation method that incorporates temporal prior information to impute the missing traffic data is proposed. Specifically, the low-rank representation theory is first employed to model the global spatial correlation of traffic data, and then the fused lasso regularisation is utilized to fit the temporal correlation of traffic data. In addition, to make the proposed model more robust, F-norm regularisation is used to smooth the Gaussian noise of traffic data. Furthermore, an efficient optimisation algorithm based on ADMM is developed to solve the proposed model. Finally, the extensive experiments performed on real dataset validate the effectiveness of the proposed method.