Traffic Flow Prediction Based on Spatiotemporal Potential Energy Fields

Traffic flow prediction is a fundamental problem in spatiotemporal data mining. Most of the existing studies focuses on designing statistical models to fit historical traffic data, which are purely data-driven approaches and fail to reveal the underlying mechanisms of urban traffic. To address this issue, we propose the spatiotemporal potential energy field model (ST-PEF+), which applies the field theory for human mobility to interpret the underlying mechanisms of urban traffic, and introduces the theory into data-driven deep learning models. ST-PEF+ consists of a PEF extraction module and a data-driven module. Inspired by the field theory for human mobility, the PEF extraction module adopts an algorithm to decompose the grid-based traffic flow graph into several polytree-based potential energy fields (PEFs), where traffic flows from high potential locations to low potential locations, just as water is driven by the gravity field. We also provide a theoretical analysis to ensure that the polytree decomposition algorithm can decompose any traffic flow graph. In the data-driven module, ST-PEF+ learns a spatiotemporal deep learning model to predict the dynamics of PEFs. The model adopts correlation-adaptive neural network structures, which consists of a temporal component for temporal correlations and a spatial component for spatial correlations. The temporal component employs a GRU and DCN combined structure to capture both short-term autocorrelation and long-term repeating patterns of PEFs. The spatial component extends the GAT using weighted directed attention to model the asymmetric spatial structure in PEFs. The prediction results of traffic flow are finally derived from PEFs that are predicted by the spatiotemporal deep learning model. We conduct extensive evaluations on three real-world traffic datasets. The results show that our model outperforms the state-of-the-art baselines. In addition, case studies confirm that the PEFs learned in our framework can reveal the underlying mechanisms of urban traffic, thus improving the model interpretability.