STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting
Accurate and timely traffic flow forecasting is crucial for intelligent transportation systems. This paper presents a novel deep learning model, the Spatial-Temporal Unified Graph Attention Network (STGAtt). By leveraging a unified graph representation and an attention mechanism, STGAtt effectively captures complex spatial-temporal dependencies. Unlike methods relying on separate spatial and temporal dependency modeling modules, STGAtt directly models correlations within a Spatial-Temporal Unified Graph, dynamically weighing connections across both dimensions. To further enhance its capabilities, STGAtt partitions traffic flow observation signal into neighborhood subsets and employs a novel exchanging mechanism, enabling effective capture of both short-range and long-range correlations. Extensive experiments on the PEMS-BAY and SHMetro datasets demonstrate STGAtt's superior performance compared to state-of-the-art baselines across various prediction horizons. Visualization of attention weights confirms STGAtt's ability to adapt to dynamic traffic patterns and capture long-range dependencies, highlighting its potential for real-world traffic flow forecasting applications.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Multi-View Fusion Neural Network for Traffic Demand Prediction
The extraction of spatial-temporal features is a crucial research in transportation studies, and current studies typically use a unified temporal modeling mechanism and fixed spatial graph for this purpose. However, the …
PredictionSpatio-Temporal Attention Graph Neural Network for Remaining Useful Life Prediction
Remaining useful life prediction plays a crucial role in the health management of industrial systems. Given the increasing complexity of systems, data-driven predictive models have attracted significant research interest…
Graph Neural NetworkManagementTowards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning
Spatio-temporal graph learning is a fundamental problem in modern urban systems. Existing approaches tackle different tasks independently, tailoring their models to unique task characteristics. These methods, however, fa…
DenoisingGraph LearningTemporal Attention Evolutional Graph Convolutional Network for Multivariate Time Series Forecasting
Multivariate time series forecasting enables the prediction of future states by leveraging historical data, thereby facilitating decision-making processes. Each data node in a multivariate time series encompasses a seque…
Multivariate Time Series ForecastingPredictionTime SeriesTime Series ForecastingUnified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Network
Research in deep learning models to forecast traffic intensities has gained great attention in recent years due to their capability to capture the complex spatio-temporal relationships within the traffic data. However, m…
Graph Neural NetworkTraffic Prediction