Affiliation:
1. University of Connecticut, Department of Computer Science & Engineering, Storrs, CT, USA
Abstract
Timely forecasting the urban anomaly events in advance is of great importance to the city management and planning. However, anomaly event prediction is highly challenging due to the sparseness of data, geographic heterogeneity (e.g., complex spatial correlation, skewed spatial distribution of anomaly events and crowd flows), and the dynamic temporal dependencies.
In this study, we propose M-STAP, a novel Multi-head Spatio-Temporal Attention Prediction approach to address the problem of multi-region urban anomaly event prediction. Specifically, M-STAP considers the problem from three main aspects: (1) extracting the spatial characteristics of the anomaly events in different regions, and the spatial correlations between anomaly events and crowd flows; (2) modeling the impacts of crowd flow dynamic of the most relevant regions in each time step on the anomaly events; and (3) employing attention mechanism to analyze the varying impacts of the historical anomaly events on the predicted data. We have conducted extensive experimental studies on the crowd flows and anomaly events data of New York City, Melbourne and Chicago. Our proposed model shows higher accuracy (41.91% improvement on average) in predicting multi-region anomaly events compared with the state-of-the-arts.
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction
Cited by
7 articles.
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1. Weather Knows What Will Occur: Urban Public Nuisance Events Prediction and Control with Meteorological Assistance;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24
2. STICAP
: Spatio-temporal Interactive Attention for Citywide Crowd Activity Prediction;ACM Transactions on Spatial Algorithms and Systems;2024-01-15
3. How to Be a Well-Prepared Organizer: Studying the Causal Effects of City Events on Human Mobility;Communications in Computer and Information Science;2024
4. sUrban;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies;2023-09-27
5. Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster;Proceedings of the ACM Web Conference 2023;2023-04-30