TL;DR
Results suggest that STIF-DGCN can serve as an efficient and interpretable prediction module for large-scale highway traffic forecasting and real-time decision support.
A novel hybrid deep learning framework suitable for cloud-based control platforms, providing a foundational algorithmic solution for vehicle-infrastructure cooperative perception and decision-making and suggests potential for integration into intelligent transportation cloud control platforms.
G-VTM, a generalized vision-trajectory model, is proposed, which captures global map semantics while modeling scenario-and direction-aware interaction based on intuitive visual perception and achieves strong generalized performance under heterogeneous traffic conditions.
Pedestrian trajectory prediction plays a crucial role in intelligent transportation and autonomous driving applications. This task remains challenging due to the complex spatiotemporal correlations involved. Existing methods typically extract spatial and temporal information independently, overlooking their coupling re...
A non-local multi-head spatiotemporal attention based long short-term memory model (NL-MHA-LSTM) is introduced which employs an attention mechanism to assign context weights to relevant neighbor vehicles and extends beyond pairwise effects to model long-range dependencies.
Results across the reported 15 and 30 min settings indicate that event-conditioned topology and lag-aware heterogeneous attention can improve traffic-flow forecasting on this hybrid real–simulation benchmark, indicating potential for pilot-zone applications rather than confirming real-world deployment performance.
This paper aims to propose attention-based dynamic graph convolutional recurrent neural network (ADGCRNN) for highway traffic flow prediction, which outperforms state-of-the-art baseline models and realizes multiresolution temporal fusion via self-attention.
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