Incorporating Dynamicity of Transportation Network with Multi-Weight Traffic Graph Convolution for Traffic Forecasting
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Graph Convolutional Networks (GCN) have given the ability to model complex spatial and temporal dependencies in traffic data and improve the performance of predictions. In many studies, however, features that can represent the transportation networks such as speed limit, distance, and flow direction are overlooked. Learning without these structural features may not capture spatial dependencies and lead to low performance especially on roads with unusual characteristics. To address this challenge, we suggest a novel GCN structure that can incorporate multiple weights at the same time. The proposed model, Multi-Weight Traffic Graph Convolutional Networks (MW-TGC) conduct convolution operation on traffic data with multiple weighted adjacency matrices and combines the features obtained from each operation. The spatially isolated dimension reduction operation is conducted on the combined features to learn the dependencies among the features and reduce the size of output to a computationally feasible level. The output of multi-weight graph convolution is given to the Long Short-Term Memory (LSTM) to learn temporal dependencies. Experiment on two real-world datasets for 5min average speed of Seoul is conducted to evaluate the performance. The result shows that the proposed model outperforms the state-of-the-art models and reduces the inconsistency of prediction among roads with different characteristics.
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