Adaptive Parameter-Harmonized Dynamic Hypergraph Spatio-Temporal Prediction for Distributed Traffic Scenarios
TL;DR
A novel Adaptive Parameter Coordination Dynamic Hypergraph Spatio-Temporal Prediction (ADH-STPC) framework for decentralized learning, which provides the core topology for adaptive collaboration, while DHCGA captures spatio-temporal traffic features, and together they drive the model to achieve optimal performance.
Abstract
Traffic flow prediction is a critical task in Intelligent Transportation Systems (ITS), essential for alleviating traffic congestion and optimizing resource allocation. While graph neural network-based methods have achieved significant success in modeling pairwise relationships between nodes, they often struggle to capture the dynamic, high-order spatio-temporal dependencies inherent in traffic networks. Furthermore, in distributed real-world scenarios, data heterogeneity among different clients often leads to negative transfer in traditional federated learning methods, thereby limiting model performance. To address these challenges, this paper proposes a novel Adaptive Parameter Coordination Dynamic Hypergraph Spatio-Temporal Prediction (ADH-STPC) framework for decentralized learning. This framework centers on the Client-Adaptive Parameter Sharing (CAPS) module, which coordinates decentralized collaboration by quantifying the similarity of global and local feature distributions, ensuring that parameter propagation occurs only between compatible clients, thereby suppressing negative propagation. Additionally, to accurately predict changes in traffic flow, we designed the Dynamic Hypergraph Convolution Architecture (DHCGA), which captures higher-order nonlinear dependencies by adaptively evolving hypergraph structures, thereby uncovering latent relationships among features. CAPS provides the core topology for adaptive collaboration, while DHCGA captures spatio-temporal traffic features, and together they drive the model to achieve optimal performance. Extensive experiments on two large-scale real-world traffic datasets validate the effectiveness of this framework. Compared to existing state-of-the-art baseline models, ADH-STPC demonstrates superior performance: achieving significant reductions across all key metrics on the BJSubway dataset, including a 15.1% decrease in MAE, a 15.2% reduction in RMSE, and a 7.1% improvement in MAPE.