为提高公路隧道交通事故预测准确率，以西汉高速秦岭某隧道群的496起交通事故作为研究对象，对影响公路隧道交通事故预测的道路环境因素进行相关性分析，针对不同预测类别选定具有显著影响的主要变量，通过贝叶斯模型、随机森林模型、BP神经网络模型和支持向量机模型分别对公路隧道交通事故形态、严重程度、伤亡情况和持续时间进行预测，根据准确率和稳定性确定出最优预测模型。研究结果表明： 1）随机森林模型在预测公路隧道交通事故形态时最为可靠，准确率约为84%； 2）在对公路隧道交通事故严重程度和伤亡情况进行预测时可优先考虑贝叶斯模型，其对重大或特大事故的预测准确率高达50%； 3）选择随机森林模型作为公路隧道交通事故持续时间的预测模型，绝对误差为20 min时模型准确率将超过70%。

In this paper, 496 traffic accidents in Qinling Tunnel group on Xi′anHanzhong Expressway are taken as study objects to analysis the road environmental factors affecting traffic accident prediction of highway tunnels; the main variables having significant influence on different prediction class are selected; the traffic accident form, accidental severity, accident casualty and accident duration of highway tunnel are predicted by naive Bayesian model, random forest model, BP neural network model and support vector machine model, respectively; and the optimal prediction model is determined based on accuracy and stability of each model. The study results show that: (1) The random forest model is most suitable for prediction of highway tunnel accident form, and its accuracy reaches about 84%. (2) The naive Bayesian model should be given priority to the prediction of traffic accident severity and casualty of highway tunnels, whose accuracy is as high as 50% when predicting major or extraordinary serious accidents. (3) When the random forest model is selected as the prediction model for highway tunnel traffic accident duration, the accuracy exceeds 70% when the absolute error is 20 min.