清华大学学报（自然科学版）2026，Vol.66Issue(2)：241-256,16.DOI:10.16511/j.cnki.qhdxxb.2026.27.011
基于混合模型的危化品道路运输风险耦合时变效应
Hybrid model of coupled time-varying effects for risks in the road transport of hazardous chemicals
摘要
Abstract
[Objective]The evolution of accidents during road transportation of hazardous chemicals based on the conditions of vehicles,roads,and the environment is highly complex and poses a serious threat to public safety.Clarifying the intensity of the interaction and the coupling characteristics of different risk factors in the transportation system is crucial for preventing related accidents and enhancing transportation safety.However,traditional models rarely explain the mechanism of interaction between risk factors at the micro level,and the risk calculation process generally requires substantial high-quality data and information support while exhibiting limitations in handling event uncertainty.[Methods]To address these issues and accurately describe the dynamic characteristics of accident risk evolution,this paper proposes a risk evolution assessment method for the road transportation of hazardous chemicals based on a hybrid model.First,risk factors are identified based on the accident database,and risk coupling types are determined.Second,the network scale-interaction degree(N-K)model is used to calculate the coupling degree for different risk types,quantifying the interaction intensity among risk factors.Thereafter,a dynamic Bayesian network(DBN)containing coupling nodes is constructed based on the different coupling types and degrees.The node parameters are determined based on multiple databases,including a comprehensive database of hazardous chemical road transportation accidents,an environmental monitoring database for key road sections,and existing knowledge.DBN analysis is performed considering time steps.Finally,the time characteristics and potential mechanisms of risk evolution across different risk coupling types are revealed using risk-level indicators,based on which risk-prevention strategies are proposed.[Results]Analysis of coupling degree shows that the coupling degree of multi-factor coupling nodes is significantly higher than that of dual-factor coupling nodes.In the dual-factor coupling nodes,the degree of the driver-environment coupling node is the highest.Driver factors are more likely to couple with other factors,thereby increasing the risk of traffic accidents.The DBN analysis shows that the probability of risk coupling nodes gradually increases over time.Illegal operations and bad weather significantly affect the corresponding risk coupling nodes.Among all the risk coupling nodes,driver-environment coupling has the highest probability,indicating that driver-environment risk coupling is the most likely factor to cause accidents in the transportation of hazardous chemicals.The risk level calculation shows that the driver-environment coupling factor has the greatest impact on accident risk.Although the hazardous chemical factor does not have the highest degree of coupling,it must still be considered because of its relatively high probability of occurrence.Furthermore,although the coupling degree of multi-factor coupling nodes is higher,due to the relatively low probability of multiple factors occurring simultaneously,the risk intensity level is actually lower.[Conclusions]The proposed hybrid model reveals the interaction intensity and coupling characteristics of different risk factors at the micro level.By considering the time step,the model effectively captures the evolution of accident probability driven by different types of risk coupling.Applying the hybrid model can address the challenge of assessing accident likelihood in scenarios involving the coupling of different risk factors,providing a reference to help decision-makers and safety managers effectively prevent related accidents.关键词
道路运输/风险演化/危化品/网络规模-相互作用度模型/动态Bayes网络Key words
road transportation/risk evolution/hazardous chemicals/network scale-interaction degree model/dynamic Bayesian network分类
资源环境引用本文复制引用
骆成,王雅轩..基于混合模型的危化品道路运输风险耦合时变效应[J].清华大学学报（自然科学版）,2026,66(2):241-256,16.基金项目
四川省哲学社会科学重点实验室"智慧应急管理重点实验室"开放课题(2023ZHYJGL-10) （2023ZHYJGL-10）
西华大学人才项目(Z242131) （Z242131）
