车辆轨迹中的隐性位置异常数据检测  

Implicit Positional Anomaly Data Detection in Vehicle Trajectories

作　　者：康军[1] 吴子豪 崔晟靖 任海冰 KANG Jun;WU Zi-Hao;CUI Sheng-Jing;REN Hai-Bing(School of Information Engineering,Chang’an University,Xi’an 710064,China)

机构地区：[1]长安大学信息工程学院,西安710064

出　　处：《计算机系统应用》2023年第12期180-188,共9页Computer Systems & Applications

基　　金：陕西省重点研发计划(2020ZDLGY09-02)。

摘　　要：随着智能交通的发展,大量的车辆轨迹数据被收集和存储,但这些轨迹数据总是会存在异常轨迹点数据,严重影响后续轨迹数据分析的准确性和有效性.本文发现了一类隐性的位置异常轨迹数据,此类异常数据用传统的基于移动特征阈值的检测方法难于发现,但对轨迹数据分析过程同样有着重要的影响.针对此类异常轨迹数据,本文以部分西安市出租车轨迹数据为例,提出了一种基于浮动网格和聚类方法的隐性异常轨迹数据检测方法,并实现了数据的并行化方式.实验结果展示所提方法检测隐性位置异常的数据召回率、精确率能够达到0.90,并且F1-score在0.88–0.91范围.检测出这种隐性异常轨迹数据,有利于后续的时空轨迹数据分析与应用.With the development of intelligent transportation,a large amount of vehicle trajectory data is collected and stored.However,the trajectory data always has anomalous trajectory point data,seriously affecting the accuracy and effectiveness of subsequent trajectory data analysis.This study finds a class of implicit positional anomaly trajectory data that is difficult to be detected by traditional detection methods based on movement feature thresholds but plays a vital role in trajectory data analysis.To this end,this study proposes a method to detect the implicit anomalous trajectory data based on floating grid and clustering method.The parallelization method of data is realized by taking the trajectory data of some cabs in Xi’an as an example.The experimental results show that the data recall and accuracy of the proposed method to detect the hidden location anomaly could reach 0.90,and the F1-score is in the range of 0.88–0.91.The detection of such implicit anomalous trajectory data is beneficial to subsequent analysis and application of spatio-temporal trajectory data.

关 键 词：智能交通 轨迹数据 异常检测 聚类 K-MEANS 检测方法 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] U495[自动化与计算机技术—计算机科学与技术]