随机森林算法在交通状态判别中的应用

Application of random forest algorithm to traffic state identification

随机森林算法随机选择多个决策树构成森林,算法分类结果由这些决策树投票得到,在运算量没有显著增加的前提下提高了预测精度,是一种目前比较流行的组合分类器算法。随机森林算法不仅可以用来做分类,也可用来做回归预测,是机器学习、计算机视觉等领域内应用极为广泛的一个算法。该文将随机森林分类算法用于交通状态判别,利用实测数据进行模型训练和验证,并用袋外数据计算判别正确率,实验结果表明该方法具有可行性,为交通状态判别提供了一种新思路。

The random forest algorithm selects multiple decision trees randomly to constitute a forest, and the algorithm classification results are obtained by voting of these decision trees. The prediction accuracy is improved under the premise of no significant increase in computation, and it is a popular combination classifier algorithm at present. The random forest algorithm can be used for not only classification, but also regression prediction. It is most widely used in the fields of machine learning, computer vision and so on. This paper applies the random forest algorithm to the traffic state identification, and by using the real measured data, the model training and validation are carried out. The discriminant accuracy is calculated with the data out of the bag. The experimental results show that the method is feasible and provides a new idea for traffic state identification.