基于FTA-BN的综合交通枢纽中地铁车站安全风险评价

以综合交通枢纽中的地铁车站安全为研究目标，综合考虑综合交通枢纽中特殊的客流组成和乘客特征，以踩踏、火灾、水灾、公共卫生和大面积滞留5类易发风险事故作为研究对象，基于FTA-BN方法对其影响因素进行分析，识别其风险因素，建立相应的事故树模型，转化为贝叶斯网络模型进行风险评价；引入三角模糊数处理专家自然语言，得到贝叶斯网络中的先验概率和条件概率分布，然后通过贝叶斯网络模型进行网络推理计算和敏感性分析，找出地铁车站中的薄弱部分，制定相应的风险管控措施，从而提高枢纽中地铁车站对于紧急事件的应对能力。以广州南站地铁车站为例进行快速评价，结果表明：广州南站在公共卫生安全方面存在一定危险，发生概率为42.98%，且较易发生大面积滞留事件，可能性为30.40%。

Safety Risk Assessment of a Subway Station in an Integrated Transportation Hub Based on FTA-BN

This study considers subway station safety in an integrated transportation hub as the research objective and comprehensively examines the special passenger flow composition and passenger characteristics. Five accident risk-prone types were considered, namely, stampedes, fires, floods, public health, and large-area detention. The study analyzed its influencing factors based on the FTA-BN method, identified the risk factors, and established a corresponding fault tree model, which was later transformed into a Bayesian network model for risk assessment. Prior probability and conditional probability distributions in Bayesian networks were obtained by introducing an expert natural language for triangular fuzzy number processing. Subsequently, the Bayesian network model was used to perform network reasoning calculations and sensitivity analysis. The results helped identify the weak parts of the subway station and formulate corresponding risk control measures to improve the emergency response of the subway station in the hub. The subway station of Guangzhou South Metro Station was considered as an example for rapid evaluation, and the evaluation results showed that the station was prone to a certain amount of risk in terms of public health safety with a probability of 42.98%, and was prone to large-area detention with a probability of 30.40%.