| Title | An Attention-Mechanism-Based Traffic Flow Prediction Scheme for Smart City |
|---|---|
| ID_Doc | 36674 |
| Authors | Hu, X; Wei, X; Gao, Y; Zhuang, WQ; Chen, MZ; Lv, HB |
| Title | An Attention-Mechanism-Based Traffic Flow Prediction Scheme for Smart City |
| Year | 2019 |
| Published | |
| Abstract | With the continuous development of smart city, the task of base station traffic flow prediction has become an urgent problem to be solved. In the traffic flow prediction, historical traffic data can provide valuable and important information. However, traditional algorithms cannot allocate reasonable attention to historical traffic during model fitting and prediction. In other words, the attention capability of models is not considered. In order to handle this issue, this paper proposes a base station traffic flow prediction scheme based on Long Short-Term Memory with attention mechanism (A-LSTM). The designed A-LSTM scheme contains three steps. Firstly, the collected base station data should be cleaned up. Subsequently, feature engineering is performed. Finally, the traffic flow prediction algorithm based on the A-LSTM algorithm is realized. It is noted that missing values padding can also be performed under the framework of the A-LSTM. Experiments on real dataset show that the adopted A-LSTM can more effectively model the long term sequential relationship in the data, comparing with the LSTM and the other related algorithms, and achieve the best performance in base station traffic flow prediction for the dataset. |
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