EviPhys: Evidence-Aware Multimodal Representation Learning with Physics-Guided Graph Modeling

Learning reliable representations from structured spatio-temporal data is crucial for traffic flow prediction. However, three major challenges remain: effectively leveraging structural and semantic information from traffic data, improving the robustness of traffic representation under noisy and uncertain observations, and incorporating physical knowledge to enhance prediction reliability. To address these challenges, this paper proposes **EviPhys**, an **Evi**dence-aware multimodal representation learning framework with **Phys**ics-guided graph modeling. First, the Multimodal Representation Fusion Module (MRFM) converts historical traffic measurements into a three-channel image that encodes traffic level, temporal variation, and sensor-axis variation, and aligns the image with a state-aware text prompt through a frozen Vision-Language Model (VLM) to obtain macroscopic traffic semantics. Second, the Dempster-Shafer Evidence Reasoning Module (DSERM) maps each sensor's historical window to a Basic Probability Assignment (BPA) and uses Deng entropy to generate reliability-aware microscopic representations. Third, the Evidence Semantic Graph Learning Module (ESGLM) fuses the macroscopic semantics and microscopic evidence to construct a directed propagation graph. Fourth, the Physics-Guided Prediction Module (PGPM) recursively predicts traffic flow increments and regularizes the predicted flow sequence with a graph-diffusion prior defined on the physical sensor graph, encouraging topology-consistent flow evolution and stable long-horizon prediction. Experiments on four PEMS benchmarks and two large-scale LargeST datasets demonstrate the effectiveness and scalability of EviPhys, achieving average reductions of 2.60%, 1.08%, and 2.17% in MAE, RMSE, and MAPE compared with the second-best results on the PEMS benchmarks. On the SD and GBA datasets, EviPhys achieves the best performance across all reported horizons and metrics, with average reductions of 9.57%, 7.73%, and 11.57% in MAE, RMSE, and MAPE on SD, and 7.80%, 4.41%, and 21.47% on GBA, respectively. Code is available at https://anonymous.4open.science/r/EviPhys-CC32.