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

With the advent of artificial intelligence (AI) and machine learning (ML),
various domains of science and engineering communites has leveraged data-driven
surrogates to model complex systems from numerous sources of information
(data). The proliferation has led to significant reduction in cost and time
involved in development of superior systems designed to perform specific
functionalities. A high proposition of such surrogates are built extensively
fusing multiple sources of data, may it be published papers, patents, open
repositories, or other resources. However, not much attention has been paid to
the differences in quality and comprehensiveness of the known and unknown
underlying physical parameters of the information sources that could have
downstream implications during system optimization. Towards resolving this
issue, a multi-source data fusion framework based on Latent Variable Gaussian
Process (LVGP) is proposed. The individual data sources are tagged as a
characteristic categorical variable that are mapped into a physically
interpretable latent space, allowing the development of source-aware data
fusion modeling. Additionally, a dissimilarity metric based on the latent
variables of LVGP is introduced to study and understand the differences in the
sources of data. The proposed approach is demonstrated on and analyzed through
two mathematical (representative parabola problem, 2D Ackley function) and two
materials science (design of FeCrAl and SmCoFe alloys) case studies. From the
case studies, it is observed that compared to using single-source and source
unaware ML models, the proposed multi-source data fusion framework can provide
better predictions for sparse-data problems, interpretability regarding the
sources, and enhanced modeling capabilities by taking advantage of the
correlations and relationships among different sources.

提出了一种基于潜在变量高斯过程的多源数据融合框架，用于解决使用多源数据进行系统优化时存在的质量和全面性差异问题，并通过数学和材料科学案例研究验证了该框架在稀疏数据问题、数据源解释性以及模型能力方面的优越性。

可解释的多源数据融合通过潜变量高斯过程

Interpretable Multi-Source Data Fusion Through Latent Variable Gaussian  Process

RGB-Thermal (RGB-T) object tracking receives more and more attention due to
the strongly complementary benefits of thermal information to visible data.
However, RGB-T research is limited by lacking a comprehensive evaluation
platform. In this paper, we propose a large-scale video benchmark dataset for
RGB-T tracking.It has three major advantages over existing ones: 1) Its size is
sufficiently large for large-scale performance evaluation (total frame number:
234K, maximum frame per sequence: 8K). 2) The alignment between RGB-T sequence
pairs is highly accurate, which does not need pre- or post-processing. 3) The
occlusion levels are annotated for occlusion-sensitive performance analysis of
different tracking algorithms.Moreover, we propose a novel graph-based approach
to learn a robust object representation for RGB-T tracking. In particular, the
tracked object is represented with a graph with image patches as nodes. This
graph including graph structure, node weights and edge weights is dynamically
learned in a unified ADMM (alternating direction method of multipliers)-based
optimization framework, in which the modality weights are also incorporated for
adaptive fusion of multiple source data.Extensive experiments on the
large-scale dataset are executed to demonstrate the effectiveness of the
proposed tracker against other state-of-the-art tracking methods. We also
provide new insights and potential research directions to the field of RGB-T
object tracking.

该研究提出了一个大规模的 RGB-T 追踪视频基准数据集和一种新的基于图的方法，以学习强大的对象表示并实现多源数据融合的自适应融合，对其他最先进的追踪方法进行比较，并提供了对 RGB-T 对象追踪领域的新见解和潜在研究方向。