Deep Learning Based Urban Anomaly Prediction from Spatiotemporal Data

Urban anomalies are unusual occurrences like congestion, crowd gathering, road accidents, natural disasters, crime, etc., that cause disturbance in society and, in worst cases, may cause loss to property or life. Prediction of these anomalies at the early stages may prevent significant loss and help the government to maintain urban sustainability. However, predicting different kinds of urban anomaly is difficult because of its dynamic nature (e.g., holiday versus weekday, office versus shopping mall) and presence in various forms (e.g., road congestion may be caused by blocked driveway or accident). This work proposes a novel integrated framework UrbanAnom that utilizes a data fusion approach to predict urban anomaly data using gated graph convolution and recurrent unit. To evaluate our urban anomaly prediction framework, we utilize multi-stream datasets of New York City’s urban anomalies, points of interest (POI), roads, calendar, and weather that were collected via smart devices in the city. The extensive experiments show that our proposed framework outperforms baseline and state-of-the-art models.