Similar papers
#diffusion models
Nov 2026
Adaptive Diffused Spatiotemporal Graph Convolution for Traffic Flow Forecasting
A novel method called adaptive diffused spatiotemporal graph convolution network (ADSTGCN) is proposed for accurate traffic flow prediction and achieves superior performance compared to other state-of-the-art methods.
Xiao Luo, Shanshan Wang, Shao-Bao Li et al.
· Journal of Transportation En... · 0 citations
#diffusion models
Sep 2026
ASTDGCN: An Adaptive Spatial-Temporal Diffusion Graph Convolutional Network for Traffic Forecasting
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...
Su-Min Li, Yi-Na Gao, Hong-Nian Zhu
· SAE technical paper series · 0 citations
Open access
Aug 2026
An enhanced dynamic spatiotemporal residual network with multi-scale gridding for network-scale traffic speed prediction
An enhanced Dynamic Spatiotemporal Residual Network (DST-ResNet) framework for network-scale traffic speed prediction is proposed, which employs a multi-scale grid partitioning strategy to segment urban road networks at varying levels of granularity, enabling precise predictions at both local and global scales.
Jia-Lin Liu, Zhi-Yi Zhang, Zhi-Ran Xu et al.
· Scientific Reports · 0 citations
Sep 2026
An interpretable recursive grey multi-frequency Fourier model for traffic flow forecasting
This study aims to address the issues of overfitting and underutilization of new information in traditional grey models for multi-frequency traffic flow forecasting. It proposes the Recursive Grey Multi-frequency Fourier Model (RGMFM) to enhance the extraction of multi-frequency periodic features and enable dynamic...
Yu Zhang, Lian-Yi Liu, Fei Deng et al.
· Grey Systems Theory and Appl... · 0 citations
Congestion Propagation Identification and Prediction Using Self Attention–Based Diffusion Convolutional Approach
A propagation probability matrix is utilizes to identify congestion propagation patterns and finds traffic behavior over 24 h, revealing critical insights into congestion trends in a selected road network and proposing a novel self attention–based diffusion convolutional network (SADCN) that effectively predicts traffi...
M. Rahman, M. Arif, Naushin Nower
· Journal of Transportation En... · 0 citations
Open access
2026
An Efficient Spike-Inspired Differential Attention Model for Traffic Flow Forecasting
Traffic flow forecasting is a fundamental task in intelligent transportation systems, and accurate multi-step prediction depends critically on effective modeling of both temporal dynamics and spatial dependencies within sensor networks. On the one hand, forecasting models must possess strong spatiotemporal interaction...
Lin-Liang Zhang, Xue-Ting Liang, Jin Li et al.
· IEEE Access · 0 citations