Traffic prediction based on spatial-temporal disentangled generative models

Understanding the underlying spatial-temporal patterns in a traffic network is crucial for accurate predictions. However, current deep learning techniques often struggle to effectively identify the generative factors hidden in spatial-temporal traffic data, leading to a lack of interpretability. To address this challenge, we have developed an innovative generative model called Spatial-Temporal Disentangled Neural Relational Inference (STDNRI) for traffic prediction and disentangling interpretable generative factors within observed traffic network data. In this study, we assume that spatial-temporal data is dominated by three generative factors: time-correlated factors, spatial-correlated factors and spatial-temporal joint correlation factors. Based on information bottleneck theory, we establish a novel objective function to maximize the disentanglement of different factors. To model the probability distributions of three factors, we propose three graph neural network encoders to represent time-correlated factors using a graph containing only self-loops, and denote spatial-correlated factors as well as spatial-temporal joint correlation factors adopting a fully-connected graph without self-loops. Then, graph neural network decoder with a spatial-temporal message passing mechanism is used to generate the final predictions. Several comprehensive experiments were conducted based on two real datasets from NYC Yellow Taxi and SHMetro. The results indicate that our STDNRI outperforms ten existing competitive models in terms of high prediction accuracy and provides excellent interpretability for generative factors. Furthermore, the disentangled interpretable factors can afford valuable insights for planners, urban developers, and other stakeholders to gain a deeper understanding of the dynamics of the traffic network, thus enabling more informed decision-making.