STHGN: Citywide Crowd Flow Prediction in Irregular Regions using Hypergraph Convolutional Network

Forecasting crowd movement accurately across an urban area is crucial for efficient traffic control and ensuring public security. Current methods involve transforming the city’s roadmap into a grid-based map, enabling Convolutional Neural Networks (CNNs) or Graph Convolutional Networks (GCNs) to capture spatio-temporal relationships efficiently. However, this approach overlooks the connection between irregularly shaped real-world areas, which can be categorized into various functional zones. In this article, we introduce a novel approach for predicting urban crowd flow named STHGN, which utilizes hypergraph convolutional networks. By constructing 3-level hypergraphs from irregular areas and adopting Hyper-GCN, we capture mobility among irregular regions. We construct the hypergraphs based on hour, day, and week, simultaneously using gated-based mechanisms to fuse various embeddings. We evaluate the efficacy of our model by contrasting it with 11 other approaches, including the most sophisticated STGs. After conducting numerous experiments, we find that STHGN outperforms these methods with higher accuracy, resulting in a reduction of approximately 6-9% in mean absolute error (MAE) for crowd flow prediction.