UApredictor: Urban Anomaly Prediction from Spatial-Temporal Data using Graph Transformer Neural Network

Abstract

Urban anomalies are abnormal events such as a blocked driveway, illegal parking, noise, crime, crowd gathering, etc. affect people and policy managers drastically if not handled in time. Prediction of these anomalies in the early stages is critical for public safety and mitigation of economic losses. However, predicting urban anomalies has various challenges like complex spatio-temporal relationships, dynamic nature, and data sparsity. This paper proposes a novel end-to-end deep learning based framework, i.e., UApredictor that utilizes stacked spatial-temporal-interaction block to predict urban anomaly from multivariate time-series data. We model the problem using an attribute graph, where we represent city regions as nodes to capture inter region spatial information using a spatial transformer. Further, to capture temporal correlation, we utilize a temporal transformer, and the interaction module retains complex interaction between spatio-temporal dimensions. Besides, the attention layer is added on the top of the spatial-temporal-interaction block that captures important information for predicting urban anomaly. We use real-world NYC-Urban Anomaly, NYC-Taxi, NYC-POI, NYC-Road Network, NYC-Demographic, and NYC-Weather datasets of New York city to evaluate the urban anomaly prediction framework. The results show that our proposed framework predicts better in terms of F-measure, macro-F1, and micro-F1 than baseline and state-of-the-art models.