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.

基于时空深度学习和多源数据融合的停车可用性预测框架提供了更准确和及时的信息，促进了更高效和可持续的城市出行。

利用 Transformer 模型融合多源交通需求数据进行城市停车预测

Leverage Multi-source Traffic Demand Data Fusion with Transformer Model  for Urban Parking Prediction

Digital soil mapping (DSM) is an advanced approach that integrates
statistical modeling and cutting-edge technologies, including machine learning
(ML) methods, to accurately depict soil properties and their spatial
distribution. Soil organic carbon (SOC) is a crucial soil attribute providing
valuable insights into soil health, nutrient cycling, greenhouse gas emissions,
and overall ecosystem productivity. This study highlights the significance of
spatial-temporal deep learning (DL) techniques within the DSM framework. A
novel architecture is proposed, incorporating spatial information using a base
convolutional neural network (CNN) model and spatial attention mechanism, along
with climate temporal information using a long short-term memory (LSTM)
network, for SOC prediction across Europe. The model utilizes a comprehensive
set of environmental features, including Landsat-8 images, topography, remote
sensing indices, and climate time series, as input features. Results
demonstrate that the proposed framework outperforms conventional ML approaches
like random forest commonly used in DSM, yielding lower root mean square error
(RMSE). This model is a robust tool for predicting SOC and could be applied to
other soil properties, thereby contributing to the advancement of DSM
techniques and facilitating land management and decision-making processes based
on accurate information.

该研究通过数字土壤制图（DSM）方法的空间 - 时间深度学习技术，提出了一个新的模型架构，具有基于卷积神经网络（CNN）模型和空间注意力机制的空间信息，以及基于长短期记忆（LSTM）网络的气候时间信息，用于欧洲土壤有机碳的预测，结果表明该模型优于常用的 DSM 方法中的随机森林等传统机器学习方法，具有更低的均方根误差（RMSE），该模型是一个强大的工具，可用于预测土壤有机碳及其他土壤属性，从而推进 DSM 技术的发展，并为基于准确信息的土地管理和决策过程提供帮助。