基于停留区域识别的子轨迹异常检测方法

Subtrajectory Anomaly Detection Method based on Stay Area Recognition

对轨迹数据进行分析和处理能够揭示移动对象的运动规律并挖掘出与其相关的隐含信息,移动对象的不规律或异常运动产生了异常轨迹数据,异常数据的出现往往意味着有特殊情况发生,隐含着更有意义的信息,快速、准确地检测异常轨迹能够服务于交通分析及事故检测等具体应用领域。针对传统轨迹异常检测方法没有充分考虑轨迹局部异常的问题,该文提出一种基于停留区域识别的子轨迹异常检测方法:①设计了一种基于密度的停留点检测算法检测轨迹集的停留点,通过寻找核心点以建立初始簇,使用核心点邻域内的点扩展当前簇,并根据簇内的时间间隔是否满足时间条件,从而检测出停留点;②根据停留点集合识别停留区域,将任意2个停留区域作为一对起点和终点区域后对轨迹进行分段;③根据分段后子轨迹的起点区域和终点区域对子轨迹集进行分组;④针对每个分组内的子轨迹,设计子轨迹异常检测算法检测异常空间子轨迹和异常时空子轨迹。在真实轨迹数据集上与传统异常检测方法进行对比,实验结果表明本文所提方法能有效地检测出异常子轨迹,并且运行时间明显低于TRAOD方法,检测准确率比TRAOD方法最高提升了23.9%;F_(1)分数值相较于ATDC和iBAT方法有明显提升,最高提升率分别为7.8%和16.1%。本研究描述的轨迹异常检测方法可以为交通运输和管理部门提供有效的决策信息,为车辆轨迹数据挖掘提供新的解决方案。

The analysis and processing of trajectory data can reveal the motion law of moving objects and dig out the hidden information related to it.Irregular or abnormal movements of moving objects generate anomalous trajectory data,and the appearance of anomalous data often means that there is a special situation,which implies more meaningful information.Rapid and accurate detection of abnormal trajectories can serve applications such as traffic analysis and accident detection.In most application scenarios,the trajectory anomaly detection method needs to pay attention to the anomaly of the trajectory segments,while the existing methods do not fully consider the local anomaly of the trajectory,and the detection results have certain shortcomings.This paper proposes a subtrajectory anomaly detection method based on stay area recognition,which aims at the problem that traditional trajectory anomaly detection methods do not fully consider the local anomaly of the trajectory.This method first designs a density-based stay point detection algorithm to detect the stay points in the trajectory set,that is to say,establishes the initial cluster by finding the core point,expands the current cluster with the points in the neighborhood of the core point,and detects the stay points by determining whether the time interval in the cluster satisfies the time conditions or not.Second,it identifies the stay areas according to the set of stay points and segments each trajectory after taking any two stay areas as a pair of start and end areas.Third,the subtrajectory set is grouped according to the start and end regions of the subtrajectories after segmentation.Finally,for the subtrajectories in each group,a subtrajectory anomaly detection algorithm is designed to detect anomalous spatial subtrajectories and anomalous spatiotemporal subtrajectories.Compared with traditional anomaly detection methods on real trajectory datasets,the experimental results show that the proposed method can effectively detect abnormal sub-trajectories,and the running time is significantly shorter than that of the TRAOD method,and the detection accuracy is up to 23.9%higher than that of the TRAOD method.Compared with the ATDC and iBAT methods,the F_(1)score is significantly improved,with the highest improvement rates of 7.8%and 16.1%,respectively.The above experimental results verify the effectiveness and practicability of the proposed method.The trajectory anomaly detection method described in this study can provide effective decisionmaking information for transportation and management departments and provide a new solution for vehicle trajectory data mining.