English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Traffic congestion anomaly detection is of paramount importance inintelligent traffic systems. The goals of transportation agencies are two-fold:to monitor the general traffic conditions in the area of interest and to locateroad segments under abnormal congestion states. Modeling congestion patternscan achieve these goals for citywide roadways, which amounts to learning thedistribution of multivariate time series (MTS). However, existing works areeither not scalable or unable to capture the spatial-temporal information inMTS simultaneously. To this end, we propose a principled and comprehensiveframework consisting of a data-driven generative approach that can performtractable density estimation for detecting traffic anomalies. Our approachfirst clusters segments in the feature space and then uses conditionalnormalizing flow to identify anomalous temporal snapshots at the cluster levelin an unsupervised setting. Then, we identify anomalies at the segment level byusing a kernel density estimator on the anomalous cluster. Extensiveexperiments on synthetic datasets show that our approach significantlyoutperforms several state-of-the-art congestion anomaly detection and diagnosismethods in terms of Recall and F1-Score. We also use the generative model tosample labeled data, which can train classifiers in a supervised setting,alleviating the lack of labeled data for anomaly detection in sparse settings.

Generative Anomaly Detection for Time Series Datasets