Abstract The risk of road transportation of flammable liquid is of great uncertainty due to the time-varying conditions of the passing locations and the environment, which leads to challenging risk analysis. In this work, a real-time risk analysis method for road tanker transportation based on the fuzzy Bayesian network (FBN) is proposed. The bow-tie model is first employed to identify hazards in flammable liquid road transportation systems and shows risk evolution. The framework of the Bayesian network (BN) is then determined accordingly. In the case that the historical statistics of accidents are limited, a probabilistic estimation model that combines expert judgment and fuzzy set theory is established to determine the prior probabilities and the conditional probabilities of the BN nodes. Case studies of typical road tanker transportation accidents were carried out to show the risk level variation with both the internal and external conditions at different moments. Sensitivities of the parent nodes were analyzed, and the critical factors leading to accidents were identified. Studies show that this method can dynamically characterize the changes in both the probabilities and the consequence levels of road tanker transport accidents. Based on the vehicle’s GPS data and the local environment, the proposed method can provide an estimation of the real-time risk for road tankers.

中文翻译：

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基于模糊贝叶斯网络的公路罐车含易燃液体实时风险分析

摘要 易燃液体道路运输的风险由于途经地点和环境的时变条件具有很大的不确定性，给风险分析带来了挑战。在这项工作中，提出了一种基于模糊贝叶斯网络（FBN）的公路罐车运输实时风险分析方法。领结模型首先用于识别易燃液体道路运输系统中的危险并显示风险演变。然后相应地确定贝叶斯网络（BN）的框架。在事故历史统计数据有限的情况下，建立结合专家判断和模糊集理论的概率估计模型，确定BN节点的先验概率和条件概率。通过对典型公路罐车运输事故的案例研究，展示了不同时刻风险水平随内部和外部条件的变化。分析父节点的敏感性，确定导致事故的关键因素。研究表明，该方法可以动态表征道路罐车运输事故发生的概率和后果等级的变化。基于车辆的 GPS 数据和当地环境，所提出的方法可以为道路罐车提供实时风险估计。研究表明，该方法可以动态表征道路罐车运输事故发生的概率和后果等级的变化。基于车辆的 GPS 数据和当地环境，所提出的方法可以为道路罐车提供实时风险估计。研究表明，该方法可以动态表征道路罐车运输事故发生的概率和后果等级的变化。基于车辆的 GPS 数据和当地环境，所提出的方法可以为道路罐车提供实时风险估计。