Traffic flow prediction model based on multi-period spatial-temporal stepwise search

doi:10.1016/j.eswa.2025.130183

Traffic flow prediction model based on multi-period spatial-temporal stepwise search

2025-11-02

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DOI：10.1016/j.eswa.2025.130183

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Traditional deep learning methods for traffic spatial-temporal prediction often require manual architecture adjustments by experts, which is both time-consuming and inefficient. Neural Architecture Search (NAS) technology offers a promising solution by automating the discovery of optimal network architectures for specific tasks. However, existing NAS approaches face significant limitations, particularly in handling multi-period features and achieving a balanced extraction of spatial and temporal features. To address the above problems, we propose a novel Traffic Flow Prediction Model based on Multi-Period Spatial-Temporal Stepwise Search (MPSTSS). First, the neural architecture search space is expanded by incorporating more classic traffic spatial-temporal feature extraction networks. Second, specialized spatial-temporal search networks are designed to handle traffic data with various periodic patterns, including weekly, daily, and recent patterns. Third, a spatial-temporal stepwise search strategy is employed to effectively balance the extraction of spatial and temporal features, and a differentiable search method is utilized to enhance search efficiency. Comprehensive experiments conducted on three public traffic datasets demonstrate that the MPSTSS model can effectively identify optimal network architectures, achieving superior prediction performance and demonstrating robust generalization capabilities.

Traffic flow prediction model based on multi-period spatial-temporal stepwise search

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