D³STN: Dynamic Delay Differential Equation Spatiotemporal Network for Traffic Flow Forecasting

Traffic flow forecasting is crucial for intelligent transportation systems. Currently, most models need to pay more attention to the delay (history) state to improve forecasting performance. In this paper, we propose a dynamic delay differential equation spatiotemporal network for traffic flow forecasting, named D3STN. First, our method integrates dynamic delay state optimization into delay differential equations to enhance delay state inputs and model forecasting performance. In addition, we propose a hybrid graph neural network and convolution multi-head attention mechanism. With the hybrid graph neural network, our method takes the multi-granularity correlation relationship into account to capture spatial characteristics from relevant nodes. With the convolution multi-head attention mechanism, our method balances the attention distribution between short-term and long-term attention. Empirical experiments are executed on highway traffic flow and metro flow datasets to evaluate the performance of our method. The results demonstrate that D3STN achieves significant advancements in traffic flow forecasting tasks. Compared with CorrSTN (baseline), D3STN makes improvements of 10.93%, 16.24% and 24.56% in terms of the MAE, RMSE and MAPE, respectively, on the HZME (outflow) dataset.