基于贝叶斯网络的集装箱公路运输风险定量评估研究  

Research on Quantitative Risk Assessment of Container Road Transportation Based on Bayesian Networks

作　　者：邓桂花 DENG Guihua(Business School,Jianghan University,Wuhan,Hubei Province,430063 China)

机构地区：[1]江汉大学商学院,湖北武汉430063

出　　处：《科技资讯》2025年第6期43-45,82,共4页Science & Technology Information

摘　　要：将故障树分析法与贝叶斯网络结合,定量评估集装箱公路运输风险。通过将故障树映射为贝叶斯网络并开发相关计算方法,实现了风险评估的量化。研究发现,运输车辆故障(0.325)、货物装卸不当(0.295)、天气条件恶劣(0.270)、信息系统平台故障(0.235)和物流节点设计过冗(0.215)是主要风险因素。贝叶斯网络的动态特性揭示了这些因素之间的复杂关系,使风险评估不仅适用于当前状态,还能够预测未来变化,为集装箱公路运输的优化提供前瞻性建议。The research combines fault tree analysis with Bayesian networks for the quantitative evaluation of risks in container road transportation.This research achieves a quantification of risk assessment by translating fault trees into Bayesian networks and devising associated computational techniques.Research has found that transportation vehicle failures(0.325),improper goods during loading and unloading(0.295),severe weather conditions(0.270),information system platform failures(0.235),and excessively redundant design of logistics nodes(0.215)as the pre⁃dominant risk factors.The Bayesian network's dynamic capabilities reveal the complex relationships among these factors,rendering the risk assessment applicable to both current scenarios and predict future changes.This approach offers proactive insights for the enhancement of container road transportation systems.

关 键 词：集装箱公路运输 故障树 贝叶斯网络 风险评估 

分 类 号：U658[交通运输工程—港口、海岸及近海工程]