基于密度聚类的出租车异常轨迹检测

出租车GPS装备的普及使用产生了大量轨迹数据。出租车异常轨迹的检测和分析,可为惩罚具有欺诈行为的出租车司机提供有益支撑。针对出租车稀疏轨迹,基于轨迹相对相似度检测异常轨迹,由于其具有不对称性,类似于DBSCAN的传统密度聚类方法无法适应此种情况,本文提出基于密度RDBSCAN算法用于出租车异常轨迹聚类检测。对于聚类得出的候选异常轨迹,结合轨迹密度异常值和轨迹长度异常值的概念,利用证据理论综合前述2个因素来判别轨迹的异常程度,进而得到异常程度最高的TOP-N异常轨迹。使用旧金山真实的出租车数据,通过提取相同起点和终点(Origin-Destination,OD)的轨迹集进行实验,实验结果表明本文提出的方法能够有效地检测到异常轨迹,并成功给出异常程度最高的TOP-N异常轨迹。

Taxi Abnormal Trajectory Detection Based on Density Clustering

The widespread use of taxi GPS equipment generates a large amount of trajectory data. The detection and analysis of taxi abnormal trajectory can provide useful support for punishing taxi drivers with fraudulent behavior. For the sparse trajectory of taxis, the anomalous trajectory is detected based on the relative similarity of trajectories. Due to its asymmetry, the traditional density clustering method similar to DBSCAN can not adapt to this situation. Therefore, this paper proposes a density-based RDBSCAN algorithm for taxis abnormal trajectory clustering detection. For the candidate anomaly trajectories obtained by clustering, this paper combines the concepts of trajectory density anomaly value and trajectory length outlier value, and uses evidence theory to synthesize the above two factors to determine the abnormal degree of trajectory, and then obtains the TOP-N anomaly trajectory with the highest degree of abnormality. Using real taxi data of San Francisco, experiments are carried out by extracting the same Origin-Destination (OD) trajectory set. The experimental results show that the proposed method can effectively detect the anomalous trajectory and successfully give the TOP-N anomaly trajectory with the highest degree of abnormality.