Interpretable Spatiotemporal Deep Learning Model for Traffic Flow Prediction Based on Potential Energy Fields

Traffic flow prediction is of great importance in traffic management and public safety, but is challenging due to the complex spatial-temporal dependencies as well as temporal dynamics. Existing work either focuses on traditional statistical models, which have limited prediction accuracy, or relies on black-box deep learning models, which have superior prediction accuracy but are hard to interpret. In contrast, we propose a novel interpretable spatiotemporal deep learning model for traffic flow prediction. Our main idea is to model the physics of traffic flows through a number of latent Spatio-Temporal Potential Energy Fields (ST-PEFs), similar to water flows driven by the gravity field. We design a novel spatiotemporal deep learning model for the ST-PEFs. The model consists of a temporal component and a spatial component. To the best of our knowledge, this is the first work that make traffic flow prediction based on potential energy fields. Experimental results on real-world traffic datasets show the effectiveness of our model compared over the existing methods. A case study confirms our model interpretability.