1 research outputs found
Urban Anomaly Analytics: Description, Detection, and Prediction
Urban anomalies may result in loss of life or property if not handled
properly. Automatically alerting anomalies in their early stage or even
predicting anomalies before happening are of great value for populations.
Recently, data-driven urban anomaly analysis frameworks have been forming,
which utilize urban big data and machine learning algorithms to detect and
predict urban anomalies automatically. In this survey, we make a comprehensive
review of the state-of-the-art research on urban anomaly analytics. We first
give an overview of four main types of urban anomalies, traffic anomaly,
unexpected crowds, environment anomaly, and individual anomaly. Next, we
summarize various types of urban datasets obtained from diverse devices, i.e.,
trajectory, trip records, CDRs, urban sensors, event records, environment data,
social media and surveillance cameras. Subsequently, a comprehensive survey of
issues on detecting and predicting techniques for urban anomalies is presented.
Finally, research challenges and open problems as discussed.Comment: Accepted by IEEE Transactions on Big Dat