An integrated risk assessment framework for multimodal transportation systems: fusing rule-augmented Bayesian networks with personalized individual semantics

초록

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.

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