Traffic Anomaly Detection Via Conditional Normalizing Flow
Traffic Anomaly Detection Via Conditional Normalizing Flow
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DOI：
10.1109/itsc55140.2022.9922061
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发表时间：
2022-10
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通讯作者：
Zhuangwei Kang;Ayan Mukhopadhyay;A. Gokhale;Shijie Wen;Abhishek Dubey
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作者：
Zhuangwei Kang;Ayan Mukhopadhyay;A. Gokhale;Shijie Wen;Abhishek Dubey
交通拥堵异常检测是智能交通系统中的一个重要环节。交通机构的目标有两个方面：监测感兴趣区域的一般交通状况和定位异常拥堵状态下的路段。对拥堵模式进行建模可以实现全市道路的这些目标，这相当于学习多变量时间序列（MTS）的分布。然而，现有的工作要么是不可扩展的或无法捕获的时空信息在MTS的同时。为此，我们提出了一个原则性和全面的框架，包括一个数据驱动的生成方法，可以执行易于处理的密度估计检测流量异常。我们的方法首先在特征空间中对片段进行聚类，然后在无监督的情况下使用条件归一化流来识别聚类级别的异常时间快照。然后，我们通过使用核密度估计的异常集群在段级别上识别异常。在人工数据集上的大量实验表明，该方法在召回率和F1分数方面明显优于几种最先进的拥塞异常检测和诊断方法。我们还使用生成模型对标记数据进行采样，这可以在监督环境中训练分类器，从而缓解稀疏环境中异常检测所需的标记数据的缺乏。
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.