Traffic congestion anomaly detection is of paramount importance in intelligent traffic systems. The goals of transportation agencies are two-fold: to monitor the general traffic conditions in the area of interest and to locate road segments under abnormal congestion states. Modeling congestion patterns can achieve these goals for citywide roadways, which amounts to learning the distribution of multivariate time series (MTS). However, existing works are either not scalable or unable to capture the spatial-temporal information in MTS simultaneously. To this end, we propose a principled and comprehensive framework consisting of a data-driven generative approach that can perform tractable density estimation for detecting traffic anomalies. Our approach first clusters segments in the feature space and then uses conditional normalizing flow to identify anomalous temporal snapshots at the cluster level in an unsupervised setting. Then, we identify anomalies at the segment level by using a kernel density estimator on the anomalous cluster. Extensive experiments on synthetic datasets show that our approach significantly outperforms several state-of-the-art congestion anomaly detection and diagnosis methods in terms of Recall and F1-Score. We also use the generative model to sample labeled data, which can train classifiers in a supervised setting, alleviating the lack of labeled data for anomaly detection in sparse settings.
翻译:在智能交通系统,交通机构的目标有两个方面:监测有关地区的一般交通状况,并将路段置于异常拥挤状态之下。建模交通拥挤模式可以实现全市公路的这些目标,这相当于学习多变时间序列的分布。然而,现有的工程不是无法扩展,就是无法同时捕捉多边贸易体系的空间时空信息。为此,我们提议了一个原则性和全面的框架,由数据驱动的基因化方法组成,可以对交通异常现象进行可移动密度估计。我们的方法在地貌空间的第一个集群部分,然后使用有条件的正常流动,在不受监督的环境下,在集群一级确定反常时间截图。然后,我们通过在异常现象集群上使用内核密度估计器来找出部分一级的异常。关于合成数据集的广泛实验表明,我们的方法大大超越了在Recall和F1-Score方面一些最先进的拥挤异常检测和诊断方法。我们的方法首先使用有条件的正常流动,在未受监督的情况下,在集群一级,我们用一个可测量的样品模型来控制地测量数据。