With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the pr...With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the problem of anomaly detection is a hot topic.Based on the development of anomalous trajectory detection of moving objects,this paper introduces the classical trajectory outlier detection（TRAOD） algorithm,and then proposes a density-based trajectory outlier detection（DBTOD） algorithm,which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense.The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented,which show the effectiveness of the algorithm.展开更多
定位技术的飞速发展催生了时空轨迹大数据,轨迹数据中往往存在着明显偏离轨迹的异常点。检测出轨迹中的异常点对提高数据质量和后续轨迹数据挖掘精度至关重要。该文提出了一种基于双向长短时记忆网络(Bidirectional Long Short-Term Mem...定位技术的飞速发展催生了时空轨迹大数据,轨迹数据中往往存在着明显偏离轨迹的异常点。检测出轨迹中的异常点对提高数据质量和后续轨迹数据挖掘精度至关重要。该文提出了一种基于双向长短时记忆网络(Bidirectional Long Short-Term Memory, Bi-LSTM)模型的轨迹异常点检测算法。首先对每个轨迹点提取一个6维的运动特征向量,然后构建了一个Bi-LSTM模型,模型输入为一定序列长度的轨迹数据特征向量,输出为轨迹点的类型结果。同时,算法采用了欠采样和过采样的组合方法缓解类别不平衡对检测性能的影响。融合了长短时记忆网络单元和双向网络,Bi-LSTM模型能够自动学习正常点和邻近异常点在运动特征上的差异。基于真实船舶轨迹标注数据的实验结果表明,该文算法的检测性能显著优于恒定速度阈值法、不考虑数据时序性的经典机器学习分类算法和卷积神经网络模型,尤其是召回率达到了0.902,验证了该文算法的有效性。展开更多
轨迹异常检测能够用来分析移动对象的异常运动行为,在交通运输、医疗监护等领域都有广泛应用.兴趣区域是移动对象集中活动的区域.本文提出了一种新的兴趣区域间异常轨迹检测算法(Detecting Anomalous Trajectories Between Interest Reg...轨迹异常检测能够用来分析移动对象的异常运动行为,在交通运输、医疗监护等领域都有广泛应用.兴趣区域是移动对象集中活动的区域.本文提出了一种新的兴趣区域间异常轨迹检测算法(Detecting Anomalous Trajectories Between Interest Regions,DATIR).不同于已有的从局部采样点进行检测的算法,DATIR算法综合考虑了轨迹的局部特征和全局特征,利用聚类方法检测兴趣区域间的异常轨迹,并能挖掘出兴趣区域间的正常路径.为了提高海量轨迹数据的异常检测效率,在DATIR算法的基础上,提出了一种并行检测算法(Parallel Algorithm for Detecting Anomalous Trajectories Between Interest Regions,PDATIR).实验结果表明,DATIR算法能够有效地检测兴趣区域间的异常轨迹,并且能够检测出兴趣区域间的正常轨迹;PDATIR算法在大数据集上表现出了明显的性能优势,具有较好的可扩展性和较高的加速比.展开更多
基金supported by the Aeronautical Science Foundation of China(20111052010)the Jiangsu Graduates Innovation Project (CXZZ120163)+1 种基金the "333" Project of Jiangsu Provincethe Qing Lan Project of Jiangsu Province
文摘With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the problem of anomaly detection is a hot topic.Based on the development of anomalous trajectory detection of moving objects,this paper introduces the classical trajectory outlier detection（TRAOD） algorithm,and then proposes a density-based trajectory outlier detection（DBTOD） algorithm,which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense.The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented,which show the effectiveness of the algorithm.
文摘异常轨迹检测是轨迹数据挖掘研究领域的一个重要研究内容,基于演化计算的异常轨迹检测算法(Top-kevolving trajectory outlier detection,TOP-EYE)是一种有效的异常轨迹检测算法。不同于其他算法采用的轨迹距离计算方法,TOP-EYE算法从轨迹的方向和密度角度出发,采用演化计算的方式检测异常。为了提高TOP-EYE算法对海量轨迹数据集异常检测的效率,本文在其基础上提出了基于MapReduce的异常轨迹检测并行算法(Parallel detecting abnormal trajectory based on TOP-EYE,PDAT-TOP),利用MapReduce并行计算的优势提高了异常轨迹检测的效率。将算法PDAT-TOP在Hadoop平台上加以实现,实验结果表明,算法PDAT-TOP能够有效地检测异常轨迹,并且具有较高的可扩展性和加速比。
文摘定位技术的飞速发展催生了时空轨迹大数据,轨迹数据中往往存在着明显偏离轨迹的异常点。检测出轨迹中的异常点对提高数据质量和后续轨迹数据挖掘精度至关重要。该文提出了一种基于双向长短时记忆网络(Bidirectional Long Short-Term Memory, Bi-LSTM)模型的轨迹异常点检测算法。首先对每个轨迹点提取一个6维的运动特征向量,然后构建了一个Bi-LSTM模型,模型输入为一定序列长度的轨迹数据特征向量,输出为轨迹点的类型结果。同时,算法采用了欠采样和过采样的组合方法缓解类别不平衡对检测性能的影响。融合了长短时记忆网络单元和双向网络,Bi-LSTM模型能够自动学习正常点和邻近异常点在运动特征上的差异。基于真实船舶轨迹标注数据的实验结果表明,该文算法的检测性能显著优于恒定速度阈值法、不考虑数据时序性的经典机器学习分类算法和卷积神经网络模型,尤其是召回率达到了0.902,验证了该文算法的有效性。
文摘轨迹异常检测能够用来分析移动对象的异常运动行为,在交通运输、医疗监护等领域都有广泛应用.兴趣区域是移动对象集中活动的区域.本文提出了一种新的兴趣区域间异常轨迹检测算法(Detecting Anomalous Trajectories Between Interest Regions,DATIR).不同于已有的从局部采样点进行检测的算法,DATIR算法综合考虑了轨迹的局部特征和全局特征,利用聚类方法检测兴趣区域间的异常轨迹,并能挖掘出兴趣区域间的正常路径.为了提高海量轨迹数据的异常检测效率,在DATIR算法的基础上,提出了一种并行检测算法(Parallel Algorithm for Detecting Anomalous Trajectories Between Interest Regions,PDATIR).实验结果表明,DATIR算法能够有效地检测兴趣区域间的异常轨迹,并且能够检测出兴趣区域间的正常轨迹;PDATIR算法在大数据集上表现出了明显的性能优势,具有较好的可扩展性和较高的加速比.
