TL;DR
This paper proposes a hybrid deep learning framework, termed GDGformer, which achieves competitive or superior performance across multiple evaluation metrics, with improved robustness under high-flow congestion and stable multi-step forecasting performance.
This study presents a deep learning–enabled framework for real-time dynamic route optimization in logistics systems, addressing fundamental limitations of traditional static routing and heuristic-based decision approaches.
Experimental results on the public PEMS04 and PEMS08 datasets demonstrate that the proposed ESDG-ALSTM model significantly improves forecasting accuracy, confirming that ESDG-ALSTM is more sensitive to abrupt events and multimodal evolution patterns and can effectively enhance prediction performance in complex traffic...
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.
An adaptive spatial–temporal diffusion graph convolutional network (ASTD-GCN) is advanced for a traffic flow prediction model that integrates adaptive graph learning, diffusion convolution, and bi-directional long short-term memory network (Bi-LSTM) with attention mechanism, showing better predictive precision in traff...
This research provides an integrated approach for dynamic spatiotemporal dependency modeling which significantly enhances the multi-step prediction accuracy in multi-step traffic flow prediction.
The Structure-Guided Spatiotemporal Attention Graph Neural Network is proposed, offering a mechanistic account of the model's decision-making process while ensuring robust forecasting by aligning attention-based reasoning with identified macroscopic dependencies and preventing over-reliance on ephemeral local noise.
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