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Real-time traffic flow prediction can not only provide travelers withreliable traffic information so that it can save people's time, but also assistthe traffic management agency to manage traffic system. It can greatly improvethe efficiency of the transportation system. Traditional traffic flowprediction approaches usually need a large amount of data but still give poorperformances. With the development of deep learning, researchers begin to payattention to artificial neural networks (ANNs) such as RNN and LSTM. However,these ANNs are very time-consuming. In our research, we improve the DeepResidual Network and build a dynamic model which previous researchers hardlyuse. We firstly integrate the input and output of the $i^{th}$ layer to theinput of the $i+1^{th}$ layer and prove that each layer will fit a simplerfunction so that the error rate will be much smaller. Then, we use the conceptof online learning in our model to update pre-trained model during prediction.Our result shows that our model has higher accuracy than some state-of-the-artmodels. In addition, our dynamic model can perform better in practicalapplications.

A Dynamic Model for Traffic Flow Prediction Using Improved DRN