O²RDL-net for joint risk classification and delay forecasting in logistics systems using interaction amplified deep

Transportation and logistics systems face frequent delays and operational risks due to congestion, human factors, and rapidly changing conditions. Predicting delays and identifying risk levels in advance is therefore essential for reliable logistics planning. Most existing methods handle delay forecasting and risk classification separately and often fail to capture complex temporal and interaction-driven patterns. To overcome these limitations, this study proposes O²RDL-Net, a load-aware and interaction-amplified deep learning framework for joint risk classification and delay forecasting. The proposed model integrates higher-order operational indicators, including the Risk Accumulation Index, Delay Sensitivity Indicator, Human-Operational Interaction Score, and Logistics System Representation, to capture cumulative risk, short-term sensitivity, and system-level interactions. A spatio-temporal attention encoder with risk regime modulation is used to improve learning stability and generalization. Experimental results show that O²RDL-Net achieves superior performance compared to baseline models, with a mean absolute error of 2.05, RMSE of 3.01, and R² of 0.91 for delay forecasting. For risk classification, the model reaches an accuracy of 92%, precision of 91%, recall of 92%, and F1-score of 91%. Interpretability analysis using SHAP and local explanations confirms that the model relies on meaningful and domain-consistent features. Overall, O²RDL-Net provides an accurate, stable, and interpretable solution for real-world logistics risk and delay management.