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A transformer-based method for vessel traffic flow forecasting
In recent years, the maritime domain has experienced tremendous growth due to the exploitation of big traffic data. Particular emphasis has been...
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Multi-attention gated temporal graph convolution neural Network for traffic flow forecasting
Real-time and accurate traffic flow forecasting plays a crucial role in transportation systems and holds great significance for urban traffic...
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Temporal super-resolution traffic flow forecasting via continuous-time network dynamics
Traffic flow forecasting is a critical task for intelligent transportation systems. However, the existed forecasting can only be conducted at certain...
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Attention-based spatial-temporal graph transformer for traffic flow forecasting
Traffic forecasting is significant for establishing intelligent traffic systems. However, the complex spatial-temporal relationships of traffic flow...
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Spatial dynamic graph convolutional network for traffic flow forecasting
The complex traffic network spatial correlation and the characteristic of high nonlinear and dynamic traffic conditions in the time are the...
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Spatiotemporal synchronous dynamic graph attention network for traffic flow forecasting
Traffic flow forecasting (TFF) is crucial for effective urban planning and traffic management. Most modeling approaches in TFF ignore the dynamic...
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Adaptive graph generation based on generalized pagerank graph neural network for traffic flow forecasting
AbstractTraffic flow forecasting is a typical multivariate time series problem that has applications in intelligent transportation systems. It...
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A correlation information-based spatiotemporal network for traffic flow forecasting
Traffic flow forecasting technology plays an important role in intelligent transportation systems. Based on graph neural networks and attention...
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A local global attention based spatiotemporal network for traffic flow forecasting
Accurate traffic forecasting is critical to improving the safety, stability, and efficiency of intelligent transportation systems. Although many...
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An Overview Based on the Overall Architecture of Traffic Forecasting
With the exponential increase in the urban population, urban transportation systems are confronted with numerous challenges. Traffic congestion is...
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Probabilistic spatio-temporal graph convolutional network for traffic forecasting
Forecasting traffic flow is crucial for Intelligent Traffic Systems (ITS), traffic control, and traffic management systems. Complex spatial and...
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Foresight plus: serverless spatio-temporal traffic forecasting
Building a real-time spatio-temporal forecasting system is a challenging problem with many practical applications such as traffic and road network...
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Attention-based spatial–temporal adaptive dual-graph convolutional network for traffic flow forecasting
Accurate traffic flow forecasting (TFF) is a prerequisite for urban traffic control and guidance, which has become the key to avoiding traffic...
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STGHTN: Spatial-temporal gated hybrid transformer network for traffic flow forecasting
Accurate traffic forecasting is a critical function of intelligent transportation systems, which remains challenging due to the complex spatial and...
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Transformer network with decoupled spatial–temporal embedding for traffic flow forecasting
Over the past few years, there has been significant research on applying Transformer models to time series prediction, yielding promising results....
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Dynamic temporal position observant graph neural network for traffic forecasting
Spatio-temporal forecasting has several applications in neurology, climate, and transportation. One classic example of such a learning assignment is...
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Attentive graph structure learning embedded in deep spatial-temporal graph neural network for traffic forecasting
AbstractA smooth traffic flow is very crucial for an intelligent traffic system. Consequently, traffic forecasting is critical in achieving...
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A novel generative corrective network structure for traffic forecasting
Traffic forecasting plays a critical role in intelligent transportation systems aiming to accurately estimate future short-term or long-term traffic...
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Multi-scale attention graph convolutional recurrent network for traffic forecasting
In the backdrop of an ever-expanding urban transportation road network, the dramatic changes in traffic flow make traffic flow forecasting become a...
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AMGCN: adaptive multigraph convolutional networks for traffic speed forecasting
AbstractTraffic speed forecasting is a crucial aspect of traffic management that requires an accurate multi spatiotemporal time series forecasting...
