Abstract
Accurate and reliable long-horizon traffic flow prediction is a cornerstone of modern Intelligent Transportation Systems (ITS), yet it remains challenging due to the complex, non-linear, and dynamic spatio-temporal dependencies inherent in traffic data. While recent Transformer-based models have shown promise, they are typically end-to-end systems that couple feature extraction and sequence prediction, which can limit their ability to fully leverage multi-faceted domain information. To address this, we propose a two-stage framework, the Feature-Enhanced iTransformer (FE-iTransformer), founded on an extract-and-enhance philosophy. The framework first employs a comprehensive Feature Enhancement Module (FEM) to distill a global context vector from spatio-temporal dynamics, periodic patterns, and temporal context-without relying on a predefined graph structure. Subsequently, an innovative per-step feature enhancement mechanism uses this global vector to enrich the original input sequence, yielding an information-rich representation that is then processed by a strong iTransformer backbone for final prediction. The effectiveness of FE-iTransformer is validated through extensive experiments: ablation studies on two classic datasets (Freeway and Urban) provide compelling evidence for the efficacy of the two-stage design, demonstrating that introducing FEM significantly improves the pure iTransformer backbone; supplementary experiments on the large-scale PEMS08 benchmark further confirm scalability and long-horizon performance, reducing Mean Absolute Error (MAE) by 19.1% over the vanilla backbone in the 120-minute forecasting task. Importantly, this study targets no-graph/weak-graph settings and does not aim to surpass graph-prior models; rather, it offers a deployment-ready, graph-free alternative when the roadway graph is unavailable or unreliable.