The characteristics of road networks cascading failures are studied to other complex networks. The evolution of mechanism and process of road networks cascading failures are studied by integrating travel prior experience and travel information based on Bayesian network inference. The Bayesian network structure of road network is generated by MATLAB program. The driver's perception of road properties change is learned based on the MATLAB program of Bayesian network parameter learning. We design the simulation algorithms and simulate the effects of different travel prior experiences and travel information by Bayesian network inference to the road network properties and cascading failures. The results show that Bayesian network inference can better reflect the quantitative impact of link choice on road network cascading failures. The research provides new idea and method for the study of cascading failures.%针对道路网络级联失效与其它复杂网络相比具有的特殊性，基于贝叶斯网络推理，融合驾驶者出行先验及出行信息，研究了道路网络级联失效的演变机理及演变过程。编制MATLAB程序生成基于道路网络的贝叶斯网络结构，根据贝叶斯网络参数学习MATLAB程序，学习了驾驶者感知路段属性变化；给出了仿真算法，仿真了贝叶斯网络推理中不同出行先验权重，以及出行信息量对道路网络属性及级联失效的影响。结果表明，贝叶斯网络推理可以较好地反映驾驶者出行路段选择对道路网络级联失效的定量影响，为研究道路网络级联失效提供了新的研究思路和方法。
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