Short-Term Spatio-Temporal Forecasting of Air Temperatures Using Deep Graph Convolutional Neural Networks

2021 ◽  
Author(s):  
Lucia García-Duarte Sáenz ◽  
Jenny Cifuentes Quintero ◽  
Geovanny Marulanda
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Short Term ◽  
Air Temperatures ◽  
Spatio Temporal

Using long short term memory and convolutional neural networks for driver drowsiness detection

2021 ◽  
Vol 156 ◽  
pp. 106107
Author(s):  
Azhar Quddus ◽  
Ali Shahidi Zandi ◽  
Laura Prest ◽  
Felix J.E. Comeau
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Long Short Term Memory

Improved Spatio-Temporal Convolutional Neural Networks for Traffic Police Gestures Recognition

2020 ◽  
Author(s):  
Zhixuan Wu ◽  
Nan Ma ◽  
Yiu-ming Cheung ◽  
Jiahong Li ◽  
Qin He ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Police ◽  
Spatio Temporal

scholarly journals Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2381
Author(s):  
Jaewon Lee ◽  
Hyeonjeong Lee ◽  
Miyoung Shin
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Accidents ◽  
Physiological Signals ◽  
Superior Performance ◽  
Short Term ◽  
Input Signals ◽  
Skin Response ◽  
Novel Method ◽  
Fully Connected

Mental stress can lead to traffic accidents by reducing a driver’s concentration or increasing fatigue while driving. In recent years, demand for methods to detect drivers’ stress in advance to prevent dangerous situations increased. Thus, we propose a novel method for detecting driving stress using nonlinear representations of short-term (30 s or less) physiological signals for multimodal convolutional neural networks (CNNs). Specifically, from hand/foot galvanic skin response (HGSR, FGSR) and heart rate (HR) short-term input signals, first, we generate corresponding two-dimensional nonlinear representations called continuous recurrence plots (Cont-RPs). Second, from the Cont-RPs, we use multimodal CNNs to automatically extract FGSR, HGSR, and HR signal representative features that can effectively differentiate between stressed and relaxed states. Lastly, we concatenate the three extracted features into one integrated representation vector, which we feed to a fully connected layer to perform classification. For the evaluation, we use a public stress dataset collected from actual driving environments. Experimental results show that the proposed method demonstrates superior performance for 30-s signals, with an overall accuracy of 95.67%, an approximately 2.5–3% improvement compared with that of previous works. Additionally, for 10-s signals, the proposed method achieves 92.33% classification accuracy, which is similar to or better than the performance of other methods using long-term signals (over 100 s).

Short-term Wind Power Prediction Based on Wavelet Feature Arrangement and Convolutional Neural Networks Deep Learning

2021 ◽  
pp. 1-1
Author(s):  
Xiaosheng Peng ◽  
Yinhuan Li ◽  
Lei Dong ◽  
Kai Cheng ◽  
Hongyu Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Wind Power ◽  
Convolutional Neural Networks ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction

scholarly journals Predicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural Networks

2019 ◽  
Vol 8 (6) ◽  
pp. 243 ◽  
Author(s):  
Yong Han ◽  
Shukang Wang ◽  
Yibin Ren ◽  
Cheng Wang ◽  
Peng Gao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Management ◽  
Deep Structure ◽  
Prediction Errors ◽  
Flow Volume ◽  
Volume Data ◽  
Short Term ◽  
Passenger Flow ◽  
Metro Network

Predicting the passenger flow of metro networks is of great importance for traffic management and public safety. However, such predictions are very challenging, as passenger flow is affected by complex spatial dependencies (nearby and distant) and temporal dependencies (recent and periodic). In this paper, we propose a novel deep-learning-based approach, named STGCNNmetro (spatiotemporal graph convolutional neural networks for metro), to collectively predict two types of passenger flow volumes—inflow and outflow—in each metro station of a city. Specifically, instead of representing metro stations by grids and employing conventional convolutional neural networks (CNNs) to capture spatiotemporal dependencies, STGCNNmetro transforms the city metro network to a graph and makes predictions using graph convolutional neural networks (GCNNs). First, we apply stereogram graph convolution operations to seamlessly capture the irregular spatiotemporal dependencies along the metro network. Second, a deep structure composed of GCNNs is constructed to capture the distant spatiotemporal dependencies at the citywide level. Finally, we integrate three temporal patterns (recent, daily, and weekly) and fuse the spatiotemporal dependencies captured from these patterns to form the final prediction values. The STGCNNmetro model is an end-to-end framework which can accept raw passenger flow-volume data, automatically capture the effective features of the citywide metro network, and output predictions. We test this model by predicting the short-term passenger flow volume in the citywide metro network of Shanghai, China. Experiments show that the STGCNNmetro model outperforms seven well-known baseline models (LSVR, PCA-kNN, NMF-kNN, Bayesian, MLR, M-CNN, and LSTM). We additionally explore the sensitivity of the model to its parameters and discuss the distribution of prediction errors.

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