Dynamic Modeling and Analysis of Bi-Directional Traffic Flows Through a Deep Spatio-Temporal Graph Neural Network

Abstract

Accurate traffic flow forecasting is critical for the efficient operation of intelligent transportation systems (ITS), as it directly supports urban management and decision-making. With the increasing complexity of urban traffic, existing models often fail to fully capture the dynamic dependencies between traffic inflows and outflows. Some treat them as a unified process, while others only explore their commonalities. Inflows and outflows exhibit distinct patterns and interactions that require more refined modeling. To improve modeling performance, we propose BiSTGNN, a novel deep spatio-temporal network model which explicitly models bidirectional traffic flows as independent stochastic processes. Our approach leverages the unique temporal and spatial dependencies of each flow direction to distinguish transitions between directions, and integrates them through a composite graph convolution framework, offering a more detailed analysis of the transfer process between flows. Additionally, we introduce an innovative dynamic graph construction method that differentiates the interactions between inflows and outflows, capturing their heterogeneous relationships. Extensive experiments on five real-world traffic datasets demonstrate that our method outperforms state-of-the-art baselines, achieving superior accuracy in predicting both inflows and outflows.