This paper presents a high-reliability congestion risk prediction framework tailored for hazardous chemical industrial parks. Unlike conventional traffic prediction studies, the proposed method integrates dynamic service capacity modeling, reservation-system control variables, and operational channel constraints into a unified binary classification formulation. Mathematical derivations, statistical validation, ablation analysis, and computational complexity analysis are systematically conducted. Experimental evaluation demonstrates strong predictive stability (AUC=0.962) and statistically significant improvement over baseline methods (p<0.01). The framework provides actionable decision support for safety-critical logistics systems.
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