摘要： 为有效利用轨迹内外部属性进行异常检测，提出一种基于BP神经网络的异常轨迹识别方法。对原始轨迹数据进行去噪处理，存储至百度云的LBS云端，基于百度地图的轨迹数据可视化网站实现轨迹显示，并通过归一化数据计算轨迹属性值。同时，将轨迹内外部特征属性作为BP神经网络算法的输入层，轨迹相似度量值作为输出层，调整隐含层系数得到训练模型，从而识别用户异常轨迹。在2个用户数据集上的仿真结果表明，该方法的异常轨迹识别准确率分别达到92.3%和100%。

关键词: 轨迹数据集, BP神经网络, 百度LBS云服务, 轨迹属性, 训练模型, 异常轨迹检测

Abstract: In order to effectively utilize the internal and external attributes of the trajectory for anomaly detection,an abnormal trajectory recognition method based on BP neural network is proposed.The original trajectory data is denoised and stored to the LBS cloud of Baidu Cloud.The trajectory data based on Baidu map is designed to visualize the trajectory of the website,and the trajectory attribute value is calculated by normalizing the data.At the same time,the internal and external feature attributes of the trajectory are used as the input layer of the BP neural network algorithm,the trajectory similarity measure is used as the output layer,and the hidden layer coefficient is adjusted to obtain the training model,thereby identifying the user's abnormal trajectory.Simulation results on two user datasets show that the accuracy of the anomaly trajectory identification of the proposed method is 92.3% and 100%,respectively.

Key words: trajectory dataset, BP neural network, Baidu LBS cloud service, trajectory attributes, training model, abnormal trajectory detection

中图分类号: 

• TP391