A Dynamic Model for Traffic Flow Prediction Using Improved DRN
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Real-time traffic flow prediction can not only provide travelers with reliable traffic information and thus save time, but also assist traffic management department to manage transportation system. It can greatly improve the efficiency of transportation. Traditional traffic flow prediction methods usually need a huge amount of data but still leaves a poor performance. With the development of deep learning, researchers begin to pay attention to artificial neural networks (ANNs) such as RNN and LSTM. However, these ANNs are very time-consuming. In our article, we improve the Deep Residual Network and build a dynamic model which previous researchers hardly use. Our result shows that our model can not only be trained efficiently but also have a higher accuracy. Additionally, our dynamic model is more suitable for practical applications.
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