Traffic volume propagating from upstream road link to downstream road link is the key parameter for designing intersection signal timing scheme. Recent works successfully used graph convolutional network (GCN) and specific time-series model to forecast traffic flow by capturing the spatial–temporal features. However, accurately predicting traffic propagation flow (tpf) is challenging, since the classical GCN model only considers the influence of adjacent road link. In complex urban road network, specific traffic propagation flow (tpf) is affected by various spatial features, such as adjacent tpf, which influences from tpf with same upstream link and tpf with same downstream link. Thus, we proposed a multi-graph learning-based model named TPP-GCN (traffic propagation prediction-graph convolutional network) in this paper to predict the traffic propagation flow in urban road network. The TPP-GCN model captures not only the temporal features but also multi-spatial features based on multi-layer convolution. We validated the model using real-world traffic flow data derived from taxi GPS data in Shenzhen, China. Finally, we compare and evaluate the proposed model with the existing models across several prediction scales.

Region-level traffic information can characterize dynamic changes of urban traffic at the macro level. Real-time region-level traffic prediction help city traffic managers with traffic demand analysis, traffic congestion control, and other activities, and it has become a research hotspot. As more vehicles are equipped with GPS devices, remote sensing data can be collected and used to conduct data-driven region-level-based traffic prediction. However, due to dynamism and randomness of urban traffic and the complexity of urban road networks, the study of such issues faces many challenges. This paper proposes a new deep learning model named TmS-GCN to predict region-level traffic information, which is composed of Graph Convolutional Network (GCN) and Gated Recurrent Unit (GRU). The GCN part captures spatial dependence among regions, while the GRU part captures the dynamic change of traffic within the region. Model verification and comparison are carried out using real taxi GPS data from Shenzhen. The experimental results show that the proposed model outperforms both the classic time series prediction model and the deep learning model at different scales.

The objective imbalance between the taxi supply and demand exists in various areas of the city. Accurately predicting this imbalance helps taxi companies with dispatching, thereby increasing their profits and meeting the travel needs of residents. The application of Graph Convolutional Networks (GCNs) in traffic forecasting has inspired the development of a spatial–temporal model for grid-level prediction of the taxi demand–supply imbalance. However, spatial–temporal GCN prediction models conventionally capture only static inter-grid correlation features. This research aims to address the dynamic influences caused by taxi mobility and the variations of other transportation modes on the demand–supply dynamics between grids. To achieve this, we employ taxi trajectory data and develop a model that incorporates dynamic GCN and Gated Recurrent Units (GRUs) to predict grid-level imbalances. This model captures the dynamic inter-grid influences between neighboring grids in the spatial dimension. It also identifies trends and periodic changes in the temporal dimension. The validation of this model, using taxi trajectory data from Shenzhen city, indicates superior performance compared to classical time-series models and spatial–temporal GCN models. An ablation study is conducted to analyze the impact of various factors on the predictive accuracy. This study demonstrates the precision and applicability of the proposed model.

Alzheimer's disease (AD) is a neurodegenerative disorder that poses a serious global threat to human health. Accurate detection of AD is critical for improving patient outcomes, yet current detection methods still exhibit significant limitations in accuracy, necessitating further improvement. In this paper, we propose an AD detection method based on multi-frequency electroencephalography (EEG) and a multi-functional connectivity graph convolutional network (MFE-FCGCN). The method conducts multi-frequency analysis of power spectral density (PSD) features across five EEG frequency bands (Delta, Theta, Alpha, Beta, Gamma) and constructs two functional connectivity networks based on mutual information and Pearson correlation coefficients. This network comprehensively captures abnormalities in brain network structures induced by AD, across different frequency bands and connectivity modes. By leveraging a multi-dimensional feature extraction and fusion strategy, the model effectively identifies EEG pattern changes associated with AD, enhancing detection accuracy. Experimental results demonstrate that this method achieves a classification accuracy of 95.09% and an AUC of 98.36% on the ds00450 dataset from the OpenNeuro database, significantly outperforming traditional approaches and validating its superior performance in AD detection. Code is available at https://github.com/brief-city/MFE-FCGCN-model.

Objective: Alzheimer’s disease (AD) is mainly identified by cognitive function deterioration. Diagnosing AD at early stages poses significant challenges for both researchers and healthcare professionals due to the subtle nature of early brain changes. Currently, electroencephalography (EEG) is widely used in the study of neurodegenerative diseases. However, most existing research relies solely on functional connectivity methods to infer inter-regional brain connectivity, overlooking the importance of spatial connections. Moreover, many existing approaches fail to fully integrate multi-frequency EEG features, limiting the comprehensive understanding of dynamic brain activity across different frequency bands. This study aims to address these limitations by developing a novel graph-based deep learning model that fully utilizes both functional and structural information from multi-frequency EEG data.Methods: This paper introduces a Multi-Frequency EEG data-based Multi-Graph Convolutional Network (MF-MGCN) model for AD diagnosis. This method integrates both functional and structural connectivity to more thoroughly capture the relationships among brain regions. By extracting differential entropy (DE) features from five distinct frequency bands of EEG signals for each segment and using graph convolutional networks (GCNs) to aggregate these features, the model effectively distinguishes between AD and healthy controls (HC).Results: The outcomes show that the developed model outperforms existing methods, achieving 96.15% accuracy and 98.74% AUC in AD and HC classification.Conclusion: These findings highlight the potential of the MF-MGCN model as a clinical tool for Alzheimer’s disease diagnosis. This approach could help clinicians detect Alzheimer’s at earlier stages, enabling timely intervention and personalized treatment plans.