基于贝叶斯网络的公路运输安全风险知识图推理,Advances in Civil Engineering

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基于贝叶斯网络的公路运输安全风险知识图推理
Advances in Civil Engineering ( IF 1.5 ) Pub Date : 2021-03-05 , DOI: 10.1155/2021/6624579
Luo Wenhui ₁ , Cai Fengtian ₁ , Wu Chuna ₁ , Meng Xingkai ₁

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准确推断有关公路运输安全风险的知识是构建知识图谱的关键方面。基于与公路交通事故相关的数据，本研究建立了贝叶斯网络模型。网络节点的初始标识是通过专家评分。然后利用现有的专家知识和K2贪婪搜索算法来构建网络结构。后来，通过期望最大化（EM）算法训练网络参数。最后，使用交汇树算法推断出关于公路运输安全风险的知识。在网络参数训练期间，将训练后的条件概率与实际概率进行比较，以验证所提出的模型与专家经验相符的有效性，从而证明了模型的有效性。此外，通过“某条道路交通事故”，可以推断出其主要的“因果链”是不当的紧急响应-人为失误事故发生，其中驾驶员失误的概率为82％，事故发生的概率为68％。 “ 举个例子。推理结果与实际事故序列之间存在一致性，这说明了所提出的知识推理方法的有效性。

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Bayesian Network-Based Knowledge Graph Inference for Highway Transportation Safety Risks

Accurate inference of knowledge about highway transportation safety risks forms a crucial aspect of building a knowledge graph. Based on the data related to highway transportation accidents, this study has developed a Bayesian network model. The initial identification of the network nodes is through expert scoring. The network structure is then constructed by utilizing the prior expert knowledge and K2 greedy search algorithm. Later, the network parameters are trained via the expectation-maximization (EM) algorithm. Finally, knowledge about highway transportation safety risks is inferred using the junction tree algorithm. A comparison is made between the trained conditional and actual probabilities during the network parameter training to verify the validity of the proposed model that accords with expert experience, thereby proving the model validity. Further, its main “causal chain” is inferred to be an improper emergency response-human failure-accident occurrence, where the probability of driver failure is 82%, and the probability of accident occurrence is 68% by taking “a certain road traffic accident” as an example. There is consistency between the inference results and the actual accident sequence that suggests the effectiveness of the proposed knowledge inference method.

更新日期：2021-03-05

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