Predicting air pollution plays a vital role in urban management and public health by providing early warnings on PM2.5, SO2, and NO2 concentrations, helping to mitigate the adverse effects of these pollutants. Traditional prediction methods, relying on physical and statistical models, often struggle to capture the complex spatio-temporal dependencies and dynamic characteristics of air pollution data. The application of deep learning methods, especially graph neural networks (GNNs), has shown promise in addressing these limitations. However, existing GNN-based methods ignore the integration of rich semantic information provided by high-resolution satellite data. To address this problem, we propose a Cross-Modality Dynamic Spatio-Temporal Graph Neural Network (CMDNet) for air pollution prediction. The model comprises two branches: a dynamic spatio-temporal graph neural network branch and a remote sensing image dynamic encoding network branch. The dynamic spatiotemporal graph neural network branch captures the spatiotemporal dependencies in air pollution data by constructing a dynamic graph structure. The remote sensing image dynamic encoding network branch extracts semantic information from high-resolution satellite data, which improves the model’s power to perceive air pollution conditions in different regions. Experiments on real-world datasets demonstrate that CMDNet achieves better air pollution prediction results than existing SOTA models, with maximum improvements of 2.4% (MAE), 1.8% (RMSE), 1.3% (CSI), 1.6% (FAR), and 1.4% (POD), providing more accurate prediction results.

Pedestrian trajectory prediction is a key component for various applications that involve human and vehicle interactions, such as autonomous driving, traffic management and smart city planning. Existing methods based on graph neural networks have limited ability to capture group interactions and precisely model complex associations among multi-agents. To solve these problems, we propose OST-HGCN, an optimized hypergraph convolutional network. It models multi-agent trajectory interactions from both temporal and spatial perspectives using hypergraph structures, and optimizes the spatio-temporal hypergraph structure to enable fine-grained analysis of multi-agent trajectory motion intentions and high-order interactions. We employ OST-HGCN to a CVAE-based prediction framework, and use the optimized hypergraph structure to predict multi-agent plausible trajectories. We conduct extensive experiments on four real trajectory prediction datasets of NBA, NFL, SDD and ETH-UCY, and verify the effectiveness of the proposed OST-HGCN.

Predicting and planning the future trajectories of various traffic participants is an important task with multiple applications, including autonomous vehicles, service robots, and intelligent transportation. However, the diversity of heterogeneous agents including pedestrians, bicycles, and vehicles in traffic scenarios presents substantial challenges to this task. Current models do not fully capture the implicit and explicit interaction relationships among these heterogeneous agents and often overlook the significance of extracting implicit correlations from agent features. To address these issues, we introduce a novel model for trajectory prediction: Heterogeneous Graph Structure Contrast Optimization (HGSCO). To accurately capture the interaction relationships among heterogeneous agents, HGSCO constructs semantic graph structures representing implicit relationships and meta-path graph structures representing explicit relationships. Then, HGSCO introduces a cross-view contrastive learning approach, which optimizes the heterogeneous graph structure by maximizing mutual information between the two types of graph structures. The model can provide precise interaction relationships among heterogeneous agents by effectively fusing these two graph representations with a gated fusion method. We utilized video data captured by camera sensors in complex environments with multiple agents to conduct experiments. Our proposed model achieved an 11.5% and 6.7% reduction in Average Displacement Error (ADE) across these datasets, respectively, and a reduction of 15.6% and 8.1% in Final Displacement Error (FDE). The results demonstrate that HGSCO significantly surpasses existing state-of-the-art methods regarding trajectory prediction accuracy.

Multivariate Time Series Classification (MTSC) has important research significance and practical value. Deep learning models have achieved considerable success in addressing MTSC problems. However, a key challenge faced by existing classification models is how to effectively consider the correlations between time series instances and across channels simultaneously, as well as how to capture the dynamic of these inter-channel correlations over time. Current methods often fall short in these aspects: on one hand, they fail to fully account for the combined effects of inter-instance and inter-channel correlations; on the other hand, they largely overlook the dynamic nature of how inter-channel correlations change over time. To address these issues, we propose a novel graph neural network model, called Similarity-Aware Graph of Graphs neural networks (SAGoG), for multivariate time series classification. This model can comprehensively consider the dependencies between channel-level and instance-level time series, it dynamically learns dependency features through graph structure evolution and graph pooling layers. We conduct experiments on the UEA dataset to validate the SAGoG model, and the results demonstrate its outstanding performance in multivariate time series classification tasks.

Abstract Trajectory prediction is essential for intelligent autonomous systems like autonomous driving, behavior analysis, and service robotics. Deep learning has emerged as the predominant technique due to its superior modeling capability for trajectory data. However, deep learning‐based models face challenges in effectively utilizing scene information and accurately modeling agent interactions, largely due to the complexity and uncertainty of real‐world scenarios. To mitigate these challenges, this study presents a novel multiagent trajectory prediction model, termed the global‐local scene‐enhanced social interaction graph network (GLSESIGN), which incorporates two pivotal strategies: global‐local scene information utilization and a social adaptive attention graph network. The model hierarchically learns scene information relevant to multiple intelligent agents, thereby enhancing the understanding of complex scenes. Additionally, it adaptively captures social interactions, improving adaptability to diverse interaction patterns through sparse graph structures. This model not only improves the understanding of complex scenes but also accurately predicts future trajectories of multiple intelligent agents by flexibly modeling intricate interactions. Experimental validation on public datasets substantiates the efficacy of the proposed model. This research offers a novel model to address the complexity and uncertainty in multiagent trajectory prediction, providing more accurate predictive support in practical application scenarios.

Multivariate time series forecasting plays an important role in many domain applications, such as air pollution forecasting and traffic forecasting. Modeling the complex dependencies among time series is a key challenging task in multivariate time series forecasting. Many previous works have used graph structures to learn inter-series correlations, which have achieved remarkable performance. However, graph networks can only capture spatio-temporal dependencies between pairs of nodes, which cannot handle high-order correlations among time series. We propose a Dynamic Hypergraph Structure Learning model (DHSL) to solve the above problems. We generate dynamic hypergraph structures from time series data using the K-Nearest Neighbors method. Then a dynamic hypergraph structure learning module is used to optimize the hypergraph structure to obtain more accurate high-order correlations among nodes. Finally, the hypergraph structures dynamically learned are used in the spatio-temporal hypergraph neural network. We conduct experiments on six real-world datasets. The prediction performance of our model surpasses existing graph network-based prediction models. The experimental results demonstrate the effectiveness and competitiveness of the DHSL model for multivariate time series forecasting.

Public transportation agencies call for an intuitive, interactive, and reusable visualization tool to detect patterns of crime (i.e. pickpockets and gangs) or missing commuters on public transportation systems. Few existing visualization techniques have visually explored mobility correlations of targets and their companions, who are characterized in diverse mobility types, by using discrete travel hints extracted from a massive amount of data. To fill this gap, a visual analytical system is provided to conduct a group-based and individual-based exploration of mobility correlations of passengers of interest, based on an auto integration of multiple queries. How passengers differ from or correlate with each other are further examined based on their spatiotemporal distributions in trajectories and ODs. Real-world case studies, as well as user feedback made by 30 participants, demonstrate the effectiveness of the system in detecting specific targets and their companions featured in diverse mobility types, or in characterizing their spatiotemporal aggregation patterns for a further tracking on public transportation systems.