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Title	Curb Parking Occupancy Prediction Based On Real-Time Fusion Of Multi-View Spatial-Temporal Information Using Graph Attention Gated Networks
ID_Doc	16834
Authors	Qian C.; Yang K.; He J.; Peng X.; Huang H.
Year	2025
Published	Applied Soft Computing, 171
DOI	http://dx.doi.org/10.1016/j.asoc.2025.112781
Abstract	Effective curb parking management is crucial for reducing traffic congestion, minimizing cruising time, and lowering pollution in smart cities through accurate, timely occupancy predictions. However, traditional spatial-temporal fusion methods often rely on sequential concatenation, leading to spatial information lag and limited real-time fusion. These methods also overlook critical spatial features for curb parking, such as accessibility metrics. To address these limitations, this study introduces the Multi-View Graph Attention Gated Recurrent Unit (MGA-GRU) model, which innovatively fuses spatial-temporal data via graph attention gated networks to enhance curb parking occupancy predictions. Validated on a curb parking dataset from Lanzhou city, China (July 2019 to December 2020), the MGA-GRU model significantly outperforms baseline methods in both short-term and long-term predictions. Ablation experiments demonstrate that each component—the graph attention gates, multi-view graph structure, and real-time fusion—significantly enhances the model's predictive accuracy, highlighting their collective importance in advancing occupancy prediction. By refining spatial-temporal fusion unit, the MGA-GRU model offers vital insights for optimizing curb parking resources and supports efficient traffic management and sustainable urban development. © 2025 Elsevier B.V.
Author Keywords	Curb parking occupancy prediction; Data fusion; Gated recurrent unit; Graph attention; Multi-view graph

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