Bayesian Network-Based Knowledge Graph Inference for Highway Transportation Safety Risks
Autor(en): |
Luo Wenhui
Cai Fengtian Wu Chuna Meng Xingkai |
---|---|
Medium: | Fachartikel |
Sprache(n): | Englisch |
Veröffentlicht in: | Advances in Civil Engineering, Januar 2021, v. 2021 |
Seite(n): | 1-11 |
DOI: | 10.1155/2021/6624579 |
Abstrakt: |
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. |
Copyright: | © 2021 Luo Wenhui et al. |
Lizenz: | Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden. |
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