Accurate short-term traffic flow forecasting is very important in smart transportation systems. In recent years, bi-directional long-short term memory (BiLSTM) has been widely used in short-term traffic flow prediction, but due to its structural characteristics, it is prone to overfitting, affecting the prediction accuracy. Given that the broad learning system (BLS) can solve the problem of overfitting, this study combines deep learning with broad learning. Furthermore, the variational mode decomposition (VMD) is introduced for noise reduction so as to minimize the interference of noise on the traffic data. By doing this, the VMD-BiLSTM-BLS short-term traffic flow prediction model is proposed in this paper. The PeMS traffic flow data is used as an example for predictive analysis, and the results show that compared with the baseline model, the ablation model, and the existing model, the proposed model has the best prediction accuracy and can better reflect the short-term traffic flow at the intersection.