Continuous spatial-temporal convolutional networks based neural control differential equations for traffic forecasting

The Neural Controlled Differential Equation (NCDE) elegantly fuses dynamical systems with deep learning, unveiling profound potential for time series modeling. Harnessing the power of neural networks to sculpt the vector fields inherent to differential equations, this methodology introduces a seamless perspective for emulating spatial-temporal dynamical paradigms. In our research, the NCDE serves as the foundational architecture. Within this construct, we adeptly integrate both Temporal Convolutional Networks (TCNs) and Graph Neural Networks (GNNs) into a framework of continuous state representation to capture long-term spatial-temporal dependencies. This avant-garde modeling approach illuminates new avenues for nuanced modeling of spatial-temporal traffic dynamics, markedly augmenting the fidelity of traffic forecasting. Empirical evaluations conducted on three publicly accessible traffic flow datasets further underscore the superior efficacy of our proposed model in traffic forecasting.