A dynamic spatial–temporal deep learning framework for traffic speed prediction on large-scale road networks
Traffic prediction plays a crucial role in an intelligent transportation system (ITS) for enabling
advanced transportation management and services. In this paper, we address the problem …
advanced transportation management and services. In this paper, we address the problem …
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 …
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 …
Long-term urban traffic speed prediction with deep learning on graphs
Traffic speed prediction is among the foundations of advanced traffic management and the
gradual deployment of internet of things sensors is empowering data-driven approaches for …
gradual deployment of internet of things sensors is empowering data-driven approaches for …
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 …
A graph attention fusion network for event-driven traffic speed prediction
Accurate road traffic speed prediction has a critical role in intelligent transportation systems
and smart cities. This task is very challenging because of the complexity of road network …
and smart cities. This task is very challenging because of the complexity of road network …
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 …
Citywide traffic speed prediction: A geometric deep learning approach
JQ James - Knowledge-Based Systems, 2021 - Elsevier
Accurate traffic speed prediction is critical to modern internet of things-based intelligent
transportation systems. It serves as the foundation of advanced traffic management systems …
transportation systems. It serves as the foundation of advanced traffic management systems …
A graph and attentive multi-path convolutional network for traffic prediction
Traffic prediction is an important and yet highly challenging problem due to the complexity
and constantly changing nature of traffic systems. To address the challenges, we propose a …
and constantly changing nature of traffic systems. To address the challenges, we propose a …
Spatial–temporal deep tensor neural networks for large-scale urban network speed prediction
Real-time traffic speed prediction is an essential component of intelligent transportation
systems applications on large-scale urban networks, eg, proactive traffic management …
systems applications on large-scale urban networks, eg, proactive traffic management …