STDEN: Towards Physics-guided Neural Networks for Traffic Flow Prediction

High-performance traffic flow prediction model deigning, a core technology for Intelligent Transportation System, is a long-standing but still challenge task for industrial and academic communities. The lack of integration between physical principle and data-driven model is an important reason for limiting the development of this field. In the literature, the physics-based methods can usually provide a clear interpretation of the dynamic process of traffic flow systems but are with limited accuracy, while data-driven methods, especially deep learning with black-box structures, can achieve improved performance but can not be fully trusted due to lacking reasonable physical basis. To bridge the gap between purely data-driven and physics-driven approaches, we propose a physics-guided deep learning model named as Spatio-Temporal Differential Equation Network (STDEN), which casts the physical mechanism of traffic flow dynamics into a deep neural network framework. Specifically, we assume the traffic flow on road networks is driven by a latent potential energy field (like water flows are driven by the gravity field), and model the spatio-temporal dynamic process of the potential energy field as a differential equation network. STDEN absorbs both the performance advantage of data-driven models and the interpretability of physics-based models, so is named a physics-guided prediction model. Experiments on three real-world traffic datasets in Beijing show that our model outperforms state-of-the-art baselines by a significant margin. A case study further verifies that our model is able to capture the mechanism of urban traffic and generate accurate results with physical meaning. The proposed framework of differential equation network modeling also has referential value for other similar applications.