为了提高实时船舶交通事故预测能力，基于支持向量机技术构建支持向量机预测模型。将历史事故数据和非事故数据按照7∶ 3的比例分成训练集样本和测试集样本，分别用来训练模型和检验预测精度，结果表明事故分类正确率为82.13%，整体分类正确率为80.34%。通过与其他预测模型分类结果比较，发现虽然支持向量机模型的非事故分类正确率稍低，但事故分类正确率明显高于其他模型。实例表明支持向量机模型用于事故预测有效。
In order to improve the ability of real-time prediction of ship traffic accidents, support vector machine (SVM) prediction model is built based on SVM technology. The historical accident data and non accident data are divided into training set samples and test set samples according to the ratio of 7∶ 3 to train the model and test the prediction accuracy respectively. The results show that the accuracy of accident classification is 82.13%, and the overall accuracy of classification is 80.34%. Compared with the classification results of other prediction models, it is found that although the accuracy of non accident classification of the SVM model is slightly lower, the accuracy of accident classification is significantly higher than that of other models. The example shows that the SVM model is effective in accident prediction.
2022,44(23): 66-69 收稿日期：2022-06-01
DOI：10.3404/j.issn.1672-7649.2022.23.013
分类号：U697
基金项目：江苏省职业教育“双师型”名师工作室培养项目（2022-09）；江苏省高等教育学会《江苏高教》专项课题（2022JSGJKT035）；江苏省教育厅基金项目（2017JSJG010）
作者简介：丁振国(1979-)，男，硕士，教授，研究方向为水上智能交通
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