Urban short-term traffic flow forecasting can help people choose the optimal route for travel and improve travel efficiency, which is necessary because the traffic congestion increasingly serious today. It is difficult to predict short-term traffic flow accurately because there are various factors can influence short-term traffic flow such as weather. To improve the accuracy of short-term traffic flow prediction, this study proposes a hybrid model based on Adaptive Neuro-Fuzzy Inference System (ANFIS). The hybrid model is combined with the periodicity knowledge model and the ANFIS model which has been driven by residual data. To verify the performance of the proposed hybrid model, it is compared with the Backward Propagating Neural Network (BPNN) model and the normal ANFIS model. The experimental results show that the hybrid model has better applicability and accuracy in traffic flow prediction.