Short-term Traffic Flow Prediction Based on the SGA-KGCN-LSTM Model

Traffic volume forecast the key to alleviating traffic congestion. However, the relationship between traffic data and outside factors makes the problem more complex. Existing traffic flow forecasting studies seldom consider the relationship between traffic data and outside factors. Therefore, we propose SGA-KGCN-LSTM to solve this problem. The model combines Savitzky-Golay (SG) filter, Knowledge Graph (KG), Graph Convolution Network (GCN), Long Short-Term Memory (LSTM), and Self-Attention Mechanism. Firstly, the SG filter can be employed to reduce the noise of traffic volume data. Then, aiming at the relationship between traffic data and outside factors, the KG theory is introduced, and the knowledge representation is applied to get the embedding of relevant knowledge. Secondly, new road features are obtained by integrating embedded information and traffic flow characteristics. GCN can be employed to acquire the spatial characteristic of traffic stream, and LSTM can be employed to extract the temporal characteristics of traffic stream. Finally, the input feature information is given enough weight by self-attention mechanism. The outcome is subsequently acquired by utilizing the fully connected layer to attain the ultimate consequence. The trajectory data of the Luohu taxi in Shenzhen are used in the experiment. The experimental consequence indicate that the SGA-KGCN-LSTM has high forecasting precision compared with the benchmark model and the ablation experiment.

Automated summary

Traffic volume forecasting is crucial for alleviating traffic congestion, however, the complex relationship between traffic data and outside factors complicates the problem. This study proposes a SGA-KGCN-LSTM model that integrates multiple techniques to address this issue. Experimental results demonstrate that the model achieves high forecasting accuracy compared to benchmark models and ablation experiments.