基于BP神经网络的异常轨迹检测方法
|
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%。
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.
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.
引文
[1] ZHENG Yu.Trajectory data mining:an overview[J].ACM Transactions on Intelligent Systems and Technology,2015,6(3):1-41.
[2] PRELIPCEAN A C,GIDOFALVI G,SUSILO Y O.Measures of transport mode segmentation of trajectories[J].International Journal of Geographical Information Science,2016,30(9):1-22.
[3] Lü Mingqi,CHEN Ling,XU Zhenxing,et al.The discovery of personally semantic places based on trajectory data mining[J].Neurocomputing,2016,173:1142-1153.
[4] GIANNOTTI F,NANNI M,PEDRESCHI D,et al.Unveiling the complexity of human mobility by querying and mining massive trajectory data[J].International Journal on Very Large Data Bases,2011,20(5):695-719.
[5] GUPTA M,GAO Jing,AGGARWAL C,et al.Outlier detection for temporal data:a survey[J].IEEE Transactions on Knowledge and Data Engineering,2014,25(1):1-20.
[6] HU Weiming,LI Xi,TIAN Guodong,et al.An incremental DPMM-based method for trajectory clustering,modeling,and retrieval[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(5):1051-1065.
[7] CAI Yingfeng,WANG Hai,CHEN Xiaobo,et al.Trajectory-based anomalous behaviour detection for intelligent traffic surveillance[J].IET Intelligent Transport Systems,2015,9(8):810-816.
[8] LAXHAMMAR R,FALKMAN G.Online learning and sequential anomaly detection in trajectories[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2014,36(6):1158-1173.
[9] SHEN Minxin,LIU D R,SHANN S H.Outlier detection from vehicle trajectories to discover roaming events[J].Information Sciences,2015,294:242-254.
[10] 温文.基于改进BP神经网络的产品质量合格率预测研究[D].广州:华南理工大学,2014.
[11] 刘良旭,乔少杰,刘宾,等.基于R-tree的高效异常轨迹检测算法[J].软件学报,2009,20(9):2426-2435.
[12] 鲍苏宁,张磊,杨光.基于核主成分分析的异常轨迹检测方法[J].计算机应用,2014,34(7):2107-2110.
[13] ZHU Jie,JIANG Wei,LIU An,et al.Time-dependent popular routes based trajectory outlier detection[C]//Proceedings of International Conference on Web Information Systems Engineering.Berlin,Germany:Springer,2015:16-30.
[14] 韩旭.基于车辆轨迹多特征的聚类分析及异常检测方法的研究[D].哈尔滨:哈尔滨工程大学,2014.
[15] 朱燕,李宏伟,樊超,等.基于聚类的出租车异常轨迹检测[J].计算机工程,2017,43(2):16-20.
[16] 毛嘉莉,金澈清,章志刚,等.轨迹大数据异常检测:研究进展及系统框架[J].软件学报,2017,28(1):17-34.
[17] 蒲春,孙政顺,赵世敏.Matlab神经网络工具箱BP算法比较[J].计算机仿真,2006(5):142-144.
[18] 闻新,周璐,李翔,等.MATLAB神经网络仿真与应用[M].北京:科学出版社,2003.
[19] ZHENG Yu,XIE Xing,MA Weiying.GeoLife:a collaborative social networking service among user,location and trajectory[J].Bulletin of the Technical Committee on Data Engineering,2011,33(2):32-39.
[2] PRELIPCEAN A C,GIDOFALVI G,SUSILO Y O.Measures of transport mode segmentation of trajectories[J].International Journal of Geographical Information Science,2016,30(9):1-22.
[3] Lü Mingqi,CHEN Ling,XU Zhenxing,et al.The discovery of personally semantic places based on trajectory data mining[J].Neurocomputing,2016,173:1142-1153.
[4] GIANNOTTI F,NANNI M,PEDRESCHI D,et al.Unveiling the complexity of human mobility by querying and mining massive trajectory data[J].International Journal on Very Large Data Bases,2011,20(5):695-719.
[5] GUPTA M,GAO Jing,AGGARWAL C,et al.Outlier detection for temporal data:a survey[J].IEEE Transactions on Knowledge and Data Engineering,2014,25(1):1-20.
[6] HU Weiming,LI Xi,TIAN Guodong,et al.An incremental DPMM-based method for trajectory clustering,modeling,and retrieval[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(5):1051-1065.
[7] CAI Yingfeng,WANG Hai,CHEN Xiaobo,et al.Trajectory-based anomalous behaviour detection for intelligent traffic surveillance[J].IET Intelligent Transport Systems,2015,9(8):810-816.
[8] LAXHAMMAR R,FALKMAN G.Online learning and sequential anomaly detection in trajectories[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2014,36(6):1158-1173.
[9] SHEN Minxin,LIU D R,SHANN S H.Outlier detection from vehicle trajectories to discover roaming events[J].Information Sciences,2015,294:242-254.
[10] 温文.基于改进BP神经网络的产品质量合格率预测研究[D].广州:华南理工大学,2014.
[11] 刘良旭,乔少杰,刘宾,等.基于R-tree的高效异常轨迹检测算法[J].软件学报,2009,20(9):2426-2435.
[12] 鲍苏宁,张磊,杨光.基于核主成分分析的异常轨迹检测方法[J].计算机应用,2014,34(7):2107-2110.
[13] ZHU Jie,JIANG Wei,LIU An,et al.Time-dependent popular routes based trajectory outlier detection[C]//Proceedings of International Conference on Web Information Systems Engineering.Berlin,Germany:Springer,2015:16-30.
[14] 韩旭.基于车辆轨迹多特征的聚类分析及异常检测方法的研究[D].哈尔滨:哈尔滨工程大学,2014.
[15] 朱燕,李宏伟,樊超,等.基于聚类的出租车异常轨迹检测[J].计算机工程,2017,43(2):16-20.
[16] 毛嘉莉,金澈清,章志刚,等.轨迹大数据异常检测:研究进展及系统框架[J].软件学报,2017,28(1):17-34.
[17] 蒲春,孙政顺,赵世敏.Matlab神经网络工具箱BP算法比较[J].计算机仿真,2006(5):142-144.
[18] 闻新,周璐,李翔,等.MATLAB神经网络仿真与应用[M].北京:科学出版社,2003.
[19] ZHENG Yu,XIE Xing,MA Weiying.GeoLife:a collaborative social networking service among user,location and trajectory[J].Bulletin of the Technical Committee on Data Engineering,2011,33(2):32-39.