基于XGboost模型的城市轨道交通列车运行速度实时异常检测研究

Real-Time Abnormal Detection of Train Operation Speed of Urban Rail Transit Based on XGboost Model

随着城市轨道交通的迅猛发展,为保证列车安全行驶,对列车速度异常检测方法研究十分必要。为此提出一种将极端梯度提升(XGboost)和异常检验方法结合的列车速度异常检测方法。首先利用现场采样的列车速度数据,对XGboost模型进行训练,然后利用交叉验证和网格搜索方法确定XGboost模型最优参数,最后利用极大似然估计和格拉布斯检验,对预测结果进行异常判定。实验结果表明:与另外4种常用模型的测试集对比,F 1值分别提高7.08%、12.9%、16.9%和2.9%,该方法在时间效率上满足列车运行实时检测要求。

With the rapid development of urban rail transit,it is necessary to study the detection method of abnormal train speed in order to ensure the safe running of trains.Therefore,a train speed anomaly detection method combining extreme gradient boost(XGboost)and anomaly detection method was proposed.Firstly,the XGboost model was trained by using the data of train speed sampled on site.Then,cross validation and grid search method were used to determine the optimal parameters of XGboost model.Finally,the maximum likelihood estimation and Grubbs test were used to determine the anomaly of the prediction results.The experimental results show that:compared with the other four common models in the test set,F1 values are increased by 7.08%,12.9%,16.9%and 2.9%respectively.Moreover,the proposed method meets the real-time detection requirements of train operation in terms of time efficiency.