Enhancing Road Sensor Data Fidelity via FMD Decomposition for Accurate Traffic Flow Prediction Using EBWO-Optimized LGC-BiGRU.

In resource-constrained intelligent transportation systems (ITSs), raw road sensor data is often affected by non-stationary noise and outliers, which severely reduces the accuracy of traffic flow prediction. To address this, this paper proposes a hybrid prediction framework that integrates Frequency Mode Decomposition (FMD), Enhanced Beluga Whale Optimization (EBWO), and the Logistic-Gaussian Circle-based Bidirectional Gated Recurrent Unit (LGC-BiGRU). The proposed FMD module introduces an adaptive frequency domain segmentation mechanism, which effectively separates noise from the data without introducing modal aliasing. EBWO employs an adaptive balancing factor mechanism to dynamically explore the spatiotemporal characteristics of traffic flow, preventing the framework from getting trapped in local optima. For predictive modeling, the LGC-BiGRU integrates an LGC Optimizer, mapping features to a circular probability space to enhance sensitivity to periodic patterns. Experimental results show that, compared with the best-performing state-of-the-art model, our framework reduces the mean absolute error (MAE) by an average of 30.6% and the root mean square error (RMSE) by 29.9%. These results confirm the framework's effectiveness and feasibility for traffic flow prediction under complex noise.