The escalation in urban private car ownership has worsened the urban parking predicament, necessitating effective parking availability prediction for urban planning and management. However, the existing prediction methods suffer from low prediction accuracy with the lack of spatial-temporal correlation features related to parking volume, and neglect of flow patterns and correlations between similar parking lots within certain areas. To address these challenges, this study proposes a parking availability prediction framework integrating spatial-temporal deep learning with multi-source data fusion, encompassing traffic demand data from multiple sources (e.g., metro, bus, taxi services), and parking lot data. The framework is based on the Transformer as the spatial-temporal deep learning model and leverages K-means clustering to establish parking cluster zones, extracting and integrating traffic demand characteristics from various transportation modes (i.e., metro, bus, online ride-hailing, and taxi) connected to parking lots. Real-world empirical data was used to verify the effectiveness of the proposed method compared with different machine learning, deep learning, and traditional statistical models for predicting parking availability. Experimental results reveal that, with the proposed pipeline, the developed Transformer model outperforms other models in terms of various metrics, e.g., Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). By fusing multi-source demanding data with spatial-temporal deep learning techniques, this approach offers the potential to develop parking availability prediction systems that furnish more accurate and timely information to both drivers and urban planners, thereby fostering more efficient and sustainable urban mobility.

翻译：城市私家车保有量的增长加剧了城市停车困境，这使得有效的停车可用性预测对城市规划和管理至关重要。然而，现有预测方法因缺乏与停车量相关的时空关联特征，且忽视了特定区域内相似停车场之间的流量模式和相关性，导致预测精度较低。针对这些挑战，本研究提出了一种集成时空深度学习与多源数据融合的停车可用性预测框架，涵盖了来自多源（例如地铁、公交、出租车服务）的交通需求数据以及停车场数据。该框架基于Transformer作为时空深度学习模型，并利用K-means聚类建立停车簇区，提取并整合与停车场相连的各种交通方式（即地铁、公交、网约车和出租车）的交通需求特征。使用真实世界实证数据验证了所提方法相较于不同机器学习、深度学习及传统统计模型在预测停车可用性方面的有效性。实验结果表明，通过所提出的流程，开发的Transformer模型在均方误差（MSE）、平均绝对误差（MAE）和平均绝对百分比误差（MAPE）等多种指标上优于其他模型。通过将多源需求数据与时空深度学习技术相融合，该方法有望开发出能为驾驶员和城市规划者提供更准确、更及时信息的停车可用性预测系统，从而促进更高效、更可持续的城市交通。