Multistep Traffic Speed Prediction from Spatial–Temporal Dependencies Using Graph Neural Networks

2021 ◽  
Vol 147 (12) ◽  
pp. 04021082
Author(s):  
Xuesong Wu ◽  
Jie Fang ◽  
Zhijia Liu ◽  
Xiongwei Wu
Keyword(s):  
Neural Networks ◽  
Speed Prediction ◽  
Traffic Speed ◽  
Graph Neural Networks ◽  
Temporal Dependencies

LSTM variants meet graph neural networks for road speed prediction

2020 ◽  
Vol 400 ◽  
pp. 34-45 ◽  
Author(s):  
Zhilong Lu ◽  
Weifeng Lv ◽  
Yabin Cao ◽  
Zhipu Xie ◽  
Hao Peng ◽  
...  
Keyword(s):  
Neural Networks ◽  
Road Speed ◽  
Speed Prediction ◽  
Graph Neural Networks

Traffic speed prediction under weekday using convolutional neural networks concepts

2017 ◽  
Author(s):  
Changhee Song ◽  
Heeyun Lee ◽  
Changbeom Kang ◽  
Wonyoung Lee ◽  
Young B. Kim ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Speed Prediction ◽  
Traffic Speed

Traffic Speed Prediction with Neural Networks

2018 ◽  
pp. 737-743
Author(s):  
Umut Can Çakmak ◽  
Mehmet Serkan Apaydın ◽  
Bülent Çatay
Keyword(s):  
Neural Networks ◽  
Speed Prediction ◽  
Traffic Speed

Features Injected Recurrent Neural Networks for Short-term Traffic Speed Prediction

2021 ◽  
Author(s):  
Licheng Qu ◽  
Jiao Lv ◽  
Wei Li ◽  
Dongfang Ma ◽  
Haiwei Fan
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Short Term ◽  
Speed Prediction ◽  
Traffic Speed

Graph Neural Networks for Prediction of Fuel Ignition Quality

2020 ◽  
Author(s):  
Artur Schweidtmann ◽  
Jan Rittig ◽  
Andrea König ◽  
Martin Grohe ◽  
Alexander Mitsos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Octane Number ◽  
Molecular Graph ◽  
Chemical Properties ◽  
Graph Representation ◽  
Structure Property ◽  
Oxygenated Hydrocarbons ◽  
Physico Chemical ◽  
Ignition Quality ◽  
Graph Neural Networks

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>

Conversational Emotion Recognition Using Self-Attention Mechanisms and Graph Neural Networks

2020 ◽  
Author(s):  
Zheng Lian ◽  
Jianhua Tao ◽  
Bin Liu ◽  
Jian Huang ◽  
Zhanlei Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Graph Neural Networks
Sign in / Sign up

Export Citation Format

Share Document