A Spatiotemporal Hierarchical Attention Mechanism-based Model for Multi-step Station-Level Crowd Flow Prediction

Abstract Multi-step station-level crowd flow prediction (Ms-SLCFP) is to predict the count of people that would depart from or arrive at subway/bus/bike stations in multiple future consecutive time periods. By providing a long term view, it benefits the decision making in related applications, such as public safety, traffic management, etc. However, performing Ms-SLCFP is challenging as complicated spatiotemporal correlations are formed among stations due to the flowing crowd. Besides, the crowd flow at a single station fluctuates a lot though the regularity is obvious at the regional level. To tackle such issues, we propose a deep neural networks-based model with spatiotemporal hierarchical attention mechanisms, called ST-HAttn for short, for Ms-SLCFP. The notable contributions are that ST-HAttn performs attention mechanisms (AM) in two ways: 1) implementing AM at both station level and regional level; 2) implementing AM to explicitly model the pairwise correlations of station-region instead of station-station. The intuition is to alleviate the negative impact on Ms-SLCFP due to the fluctuation of the crowd flow at the station level. Verified on three real-world datasets, ST-HAttn outperforms the state-of-the-art methods in terms of Ms-SLCFP.