Zhang Longmei, Lu Wei, Xue Feng, Chang Yanshuo
School of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
School of Information, Xi'an University of Finance and Economics, Xi'an 710100, China.
Math Biosci Eng. 2023 Jul 14;20(8):15075-15093. doi: 10.3934/mbe.2023675.
Trajectory outlier detection can identify abnormal phenomena from a large number of trajectory data, which is helpful to discover or predict potential traffic risks. In this work, we proposed a trajectory outlier detection model based on variational auto-encoder. First, the model encodes the trajectory data as parameters of distribution functions based on the statistical characteristics of urban traffic. Then, an auto-encoder network is built and trained. The training goal of the auto-encoder network is to maximize the generation probability of original trajectories when decoding. Once the model training is completed, we can detect the trajectory outlier by the difference between a trajectory and the trajectory generated by the model. The advantage of the proposed model is that it only needs to compute the difference between the original trajectory and the trajectory generated by the model when detecting the trajectory outlier, which greatly reduces the amount of calculation and makes the model very suitable for real-time detection scenarios. In addition, the distance threshold between the abnormal trajectory and the normal trajectory can be set by referring to the proportion of the abnormal trajectory in the training data set, which eliminates the difficulty of setting the threshold manually and makes the model more convenient to be applied in different actual scenes. In terms of effect, the proposed model has achieved more than 95% in accuracy, which is better than the two typical density-based and classification-based detection methods, and also better than the methods based on machine learning in recent years. In terms of efficiency, the model has good convergence in the training phase and the training time increases slowly with the data scale, which is better than or as the same as the comparison methods.
轨迹异常检测能够从大量轨迹数据中识别异常现象，这有助于发现或预测潜在的交通风险。在这项工作中，我们提出了一种基于变分自编码器的轨迹异常检测模型。首先，该模型根据城市交通的统计特征将轨迹数据编码为分布函数的参数。然后，构建并训练一个自编码器网络。自编码器网络的训练目标是在解码时最大化原始轨迹的生成概率。一旦模型训练完成，我们就可以通过一条轨迹与模型生成的轨迹之间的差异来检测轨迹异常。所提模型的优点在于，在检测轨迹异常时，它只需要计算原始轨迹与模型生成的轨迹之间的差异，这大大减少了计算量，使得该模型非常适合实时检测场景。此外，可以参考训练数据集中异常轨迹的比例来设置异常轨迹与正常轨迹之间的距离阈值，这消除了手动设置阈值的困难，使模型在不同的实际场景中更便于应用。在效果方面，所提模型的准确率达到了95%以上，优于两种典型的基于密度和基于分类的检测方法，也优于近年来基于机器学习的方法。在效率方面，该模型在训练阶段具有良好的收敛性，训练时间随着数据规模的增加而缓慢增长，优于或等同于比较方法。
