Incorporating multimodal context information into traffic speed forecasting through graph deep learning
Accurate traffic speed forecasting is a prerequisite for anticipating future traffic status and
increasing the resilience of intelligent transportation systems. However, most studies ignore …
increasing the resilience of intelligent transportation systems. However, most studies ignore …
Spatial-temporal graph attention networks: A deep learning approach for traffic forecasting
Traffic speed prediction, as one of the most important topics in Intelligent Transport Systems
(ITS), has been investigated thoroughly in the literature. Nonetheless, traditional methods …
(ITS), has been investigated thoroughly in the literature. Nonetheless, traditional methods …
DDP-GCN: Multi-graph convolutional network for spatiotemporal traffic forecasting
Traffic speed forecasting is one of the core problems in transportation systems. For a more
accurate prediction, recent studies started using not only the temporal speed patterns but …
accurate prediction, recent studies started using not only the temporal speed patterns but …
Short-term traffic speed forecasting based on graph attention temporal convolutional networks
G Guo, W Yuan - Neurocomputing, 2020 - Elsevier
Accurate and timely traffic forecasting is significant for intelligent transportation
management. However, existing approaches model the temporal and spatial features of …
management. However, existing approaches model the temporal and spatial features of …
Attention-based dynamic spatial-temporal graph convolutional networks for traffic speed forecasting
J Zhao, Z Liu, Q Sun, Q Li, X Jia, R Zhang - Expert Systems with …, 2022 - Elsevier
In recent years, spatial–temporal graph modeling based on graph convolutional neural
networks (GCN) has become an effective method for mining spatial–temporal dependencies …
networks (GCN) has become an effective method for mining spatial–temporal dependencies …
Global spatial-temporal graph convolutional network for urban traffic speed prediction
L Ge, S Li, Y Wang, F Chang, K Wu - Applied Sciences, 2020 - mdpi.com
Traffic speed prediction plays a significant role in the intelligent traffic system (ITS). However,
due to the complex spatial-temporal correlations of traffic data, it is very challenging to …
due to the complex spatial-temporal correlations of traffic data, it is very challenging to …
Graph attention temporal convolutional network for traffic speed forecasting on road networks
Traffic speed forecasting plays an increasingly essential role in successful intelligent
transportation systems. However, this still remains a challenging task when the accuracy …
transportation systems. However, this still remains a challenging task when the accuracy …
HetGAT: a heterogeneous graph attention network for freeway traffic speed prediction
C Jin, T Ruan, D Wu, L Xu, T Dong, T Chen… - Journal of Ambient …, 2021 - Springer
As an essential part of the modern intelligent traffic management system, traffic speed
prediction is a challenging task. In recent studies, deep neural networks (LSTM and …
prediction is a challenging task. In recent studies, deep neural networks (LSTM and …
An enhanced motif graph clustering-based deep learning approach for traffic forecasting
Traffic speed prediction is among the key problems in intelligent transportation system (ITS).
Traffic patterns with complex spatial dependency make accurate prediction on traffic …
Traffic patterns with complex spatial dependency make accurate prediction on traffic …
Multiple dynamic graph based traffic speed prediction method
Z Zhang, Y Li, H Song, H Dong - Neurocomputing, 2021 - Elsevier
Traffic speed prediction is a crucial and challenging task for intelligent transportation
systems. The prediction task can be accomplished via graph neural networks with structured …
systems. The prediction task can be accomplished via graph neural networks with structured …