CHAMFormer: Dual heterogeneous three-stages coupling and multivariate feature-aware learning network for traffic flow forecasting

Accurate traffic flow prediction is essential for intelligent transportation systems and urban planning. Traditional approaches that combine Transformer models with Graph Convolutional Networks (GCNs) or Convolutional Neural Networks (CNNs) often struggle to effectively integrate features with varying degrees of connectivity. As a result, graph-based problems do not fully utilise the capabilities of GCNs, while time-series problems fail to entirely leverage CNNs. To overcome these challenges, we introduce the Dual Heterogeneous Three-Stage Coupling and Multivariate Feature-Aware Learning Network (CHAMFormer). This architecture comprises three main components, each focusing on a distinct innovation: capturing fine-grained, short-range traffic patterns to manage immediate interactions and local bottlenecks; integrating mid-range spatial and temporal features to understand broader traffic interactions and ripple effects; and analysing complex, long-term traffic dynamics to anticipate and manage large-scale events and network behaviours across the entire system. These modules enhance GCN performance, enabling them to function more effectively alongside Transformers and Graph Neural Networks (GNNs). The CHAMFormer model incorporates a three-stage self-attention mechanism with a Skip-Connection method to improve the capture of detailed information without significantly increasing computational costs. By connecting low-level, intermediate-level, and high-level feature extractions, this model adapts well to changing traffic patterns, thereby enhancing multi-feature awareness and prediction accuracy. Extensive experiments using seven public datasets, both with and without predefined graph structures, and in multivariate and univariate scenarios demonstrate that CHAMFormer improves prediction accuracy by at least 10%–15%. To validate our proposed model, we also tested CHAMFormer in the energy domain, where it effectively handles time-series problems. Additionally, a sensitivity analysis confirms the model’s predictability and interpretability, providing valuable insights for transportation policy and infrastructure development.