TY - Jour A2 - De Barros，Alexandre Au - Zou，Xin Au - yue，Wen Long Py - 2017 DA - 2017/12/12 TI - 使用Netica SP的道路事故导致分析的贝叶斯网络方法 - 2525481 VL - 2017 - 2017基于对影响道路安全评估的因素的总体考虑，基于概率风险分析的贝叶斯网络理论应用于道路事故的因果关系。By taking Adelaide Central Business District (CBD) in South Australia as a case, the Bayesian network structure was established by integrating K2 algorithm with experts’ knowledge, and Expectation-Maximization algorithm that could process missing data was adopted to conduct the parameter learning in Netica, thereby establishing the Bayesian network model for the causation analysis of road accidents. Then Netica was used to carry out posterior probability reasoning, the most probable explanation, and inferential analysis. The results showed that the Bayesian network model could effectively explore the complex logical relation in road accidents and express the uncertain relation among related variables. The model not only can quantitatively predict the probability of an accident in certain road traffic condition but also can find the key reasons and the most unfavorable state combination which leads to the occurrence of an accident. The results of the study can provide theoretical support for urban road management authorities to thoroughly analyse the induction factors of road accidents and then establish basis in improving the safety performance of the urban road traffic system. SN - 0197-6729 UR - https://doi.org/10.1155/2017/2525481 DO - 10.1155/2017/2525481 JF - Journal of Advanced Transportation PB - Hindawi KW - ER -