An integrated risk assessment framework for multimodal transportation systems: fusing rule-augmented Bayesian networks with personalized individual semantics
提出一个融合解释结构模型、个性化个体语义和规则增强贝叶斯网络的框架，用于多式联运系统的全周期风险评估，并通过实际案例验证了其有效性。
• Proposes an integrated ISM-PIS-RABN framework for full-cycle risk assessment of multimodal transportation systems. • Introduces personalized individual semantics to quantify expert heterogeneous, mitigating group decision bias. • Employs rule-augmented Bayesian networks with factor decomposition to overcome the curse of dimensionality. • Validated via a real-world corridor case, providing actionable insights from sensitivity analysis. Multimodal transportation plays a pivotal role in international trade and logistics due to its unparalleled advantages in cost, efficiency, sustainability, and accessibility. However, its complex structure, which is characterized by multiple transfer points, long transit times, and extended routes, makes it more prone to risks than unimodal transport. Conducting scientific risk assessment and identifying critical factors are essential for improving safety. Therefore, this study proposes a risk assessment model integrating Interpretive Structural Modeling (ISM), Personalized Individual Semantics (PIS), and a Rule-Augmented Bayesian Network (RABN), forming a systematic framework covering risk factors identification, correlation analysis, and comprehensive evaluation. Potential risk factors affecting multimodal transport safety are first identified from a 4M1E (Man, Machine, Material, Management, Environment) perspective. ISM is then used to classify these factors hierarchically and construct the Bayesian network structure. By combining PIS, similarity measures, and Inverse Distance Weighting (IDW), the model processes expert judgments to determine root node probabilities. Finally, RABN performs probabilistic inference for risk assessment. The proposed framework is validated through a case study on a route in the New International Land-Sea Trade Corridor. Sensitivity analysis further identifies key risk factors and verifies parameter robustness, offering decision-making support for multimodal transportation risk management.