融合多源交通数据与自监督学习，提升城市停车空位预测精度。
Parking Availability Prediction via Fusing Multi-Source Data with A Self-Supervised Learning Enhanced Spatio-Temporal Inverted Transformer
- 构建停车聚类区，整合地铁、公交、网约车等多模式交通需求特征。
- 自监督预训练+双分支注意力机制，显著降低预测误差（MSE最低）。
- 网约车数据贡献最大，空间关联性对模型性能至关重要。
私家车数量激增加剧了城市停车难题，亟需精准高效的停车空位预测以支撑城市规划与管理。针对现有方法在建模时空依赖关系及利用多源数据方面的不足，本文提出SST-iTransformer方法。通过K-means聚类划分停车聚类区（PCZs），融合目标停车场关联的地铁、公交、网约车、出租车等多模式交通需求特征。在基础iTransformer基础上，引入基于掩码重建的自监督预训练任务，实现时空表征学习，并设计双分支注意力机制：序列注意力通过分块操作捕捉长期时序依赖，通道注意力通过逆维度建模变量间交互。基于中国成都的真实数据进行大量实验表明，SST-iTransformer优于多种基线深度学习模型（包括Informer、Autoformer、Crossformer和iTransformer），在均方误差（MSE）上达到最优，且平均绝对误差（MAE）表现优异。消融实验定量揭示各数据源重要性：网约车数据贡献最大，其次为出租车，而固定线路交通（公交/地铁）影响较小。空间相关性分析进一步证实，若不包含同聚类区内相邻停车场的历史数据，模型性能将大幅下降，凸显建模空间依赖的重要性。
原文摘要 · Abstract (English)
The rapid growth of private car ownership has worsened the urban parking predicament, underscoring the need for accurate and effective parking availability prediction to support urban planning and management. To address key limitations in modeling spatio-temporal dependencies and exploiting multi-source data for parking availability prediction, this study proposes a novel approach with SST-iTransformer. The methodology leverages K-means clustering to establish parking cluster zones (PCZs), extracting and integrating traffic demand characteristics from various transportation modes (i.e., metro, bus, online ride-hailing, and taxi) associated with the targeted parking lots. Upgraded on vanilla iTransformer, SST-iTransformer integrates masking-reconstruction-based pretext tasks for self-supervised spatio-temporal representation learning, and features an innovative dual-branch attention mechanism: Series Attention captures long-term temporal dependencies via patching operations, while Channel Attention models cross-variate interactions through inverted dimensions. Extensive experiments using real-world data from Chengdu, China, demonstrate that SST-iTransformer outperforms baseline deep learning models (including Informer, Autoformer, Crossformer, and iTransformer), achieving state-of-the-art performance with the lowest mean squared error (MSE) and competitive mean absolute error (MAE). Comprehensive ablation studies quantitatively reveal the relative importance of different data sources: incorporating ride-hailing data provides the largest performance gains, followed by taxi, whereas fixed-route transit features (bus/metro) contribute marginally. Spatial correlation analysis further confirms that excluding historical data from correlated parking lots within PCZs leads to substantial performance degradation, underscoring the importance of modeling spatial dependencies.
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