A Multi-Stream Feature Fusion Approach for Traffic Prediction
Accurate and timely traffic flow prediction is crucial for intelligent transportation systems (ITS). Recent advances in graph-based neural networks have achieved promising prediction results. However some challenges remain especially regarding graph construction and the time complexity of models. In this paper we propose a multi-stream feature fusion approach to extract and integrate rich features from traffic data and leverage a data-driven adjacent matrix instead of the distance-based matrix to construct graphs. We calculate the Spearman rank correlation coefficient between monitor stations to obtain the initial adjacent matrix and fine-tune it while training. As to the model we construct a multi-stream feature fusion block (MFFB) module which includes a three-channel network and the soft-attention mechanism. The three-channel networks are graph convolutional neural network (GCN) gated recurrent unit (GRU) and fully connected neural network (FNN) which are used to extract spatial temporal and other features respectively. The soft-attention mechanism is utilized to integrate the obtained features. The MFFB modules are stacked and a fully connected layer and a convolutional layer are used to make predictions. We conduct experiments on two real-world traffic prediction tasks and verify that our proposed approach outperforms the state-of-the-art methods within an acceptable time complexity.