增量式贝叶斯分类器在交通拥堵判别中的应用

为有效发现道路交通拥堵状态,提出基于增量式贝叶斯分类器的交通拥堵判别方法.该方法把交通拥堵是否发生看成是特殊的分类问题,选取增量式贝叶斯分类器,根据以往是否发生交通拥堵的检测数据,即分别把在发生交通拥堵和不发生交通拥堵两种情况下的交通参数作为特征参数对其进行训练,然后用得到的分类器对检测到的交通参数进行分类,判别是否发生交通拥堵.微观交通仿真数据表明该方法的可行性和有效性.

Application of incremental Bayes classifier on traffic congestion dentification

To detect the state of traffic congestion effectively, the traffic congestion identification method is presented based on the incremental Bayes classifier. Whether traffic Congestion occurs or not is considered as a special classification problem. The incremental Bayes classifier is selected and trained according to the detection data, that is, the traffic parameters of traffic congestion occurred or not, which are taken as attribute parameters. Then the traffic parameters are classified by the trained classifier to detect whether traffic congestion occurs or not. The microcosmic traffic simulation indicates that the method is not only feasible but also effective.