Modeling Local and Global Flow Aggregation for Traffic Flow Forecasting

Traffic flow forecasting is significant to traffic management and public safety. However, it is a challenging problem, because of complex spatial and temporal dependencies. Many existing approaches adopt Graph Convolution Networks (GCN) to model spatial dependencies and recurrent neural networks (RNN) to model temporal dependencies, simultaneously. However, the existing approaches mainly use adjacency matrix or distance matrix to represent the correlations between adjacent road segments, which fail to capture dynamic spatial dependencies. Besides, these approaches ignore the lag influence caused by propagation times of traffic flows and cannot model the global aggregation effect of traffic flows. In response to the limitations of the existing approaches, we model local aggregation and global aggregation of traffic flows. We propose a novel model, called the Local and Global Spatial Temporal Network (LGSTN), to forecast the traffic flows on a road segment basis (instead of regions). We first construct time-dependent flow transfer graphs to capture dynamic spatial correlations among the local traffic flows of the adjacent road segments. Next, we adopt spatial-based GCNs to model local traffic flow aggregation. Then, we propose a Lag-gated LSTM to model global traffic flow aggregation by considering free-flow reachable time matrix. Experiments on two real-world datasets have demonstrated our proposed LGSTN considerably outperforms state-of-the-art traffic forecast methods.