Self-supervision Spatiotemporal Part-Whole Convolutional Neural Network for Traffic Prediction

A New Way of Airline Traffic Prediction Based on GCN-LSTM

Frontiers in Neurorobotics ◽

10.3389/fnbot.2021.661037 ◽

2021 ◽

Vol 15 ◽

Author(s):

Jiangni Yu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Air Traffic ◽

Traffic Prediction ◽

Long Term Memory ◽

Prediction System ◽

Short Term ◽

Prediction Ability ◽

Memory Network

With the development of society and the improvement of people's material level, more and more people like to travel by airplane. If we can predict the passenger flow of an airline in advance, it can be used as an important decision-making basis for its flight route planning, crew scheduling planning and ticket price formulation in the process of management and operation. However, due to the high complexity of aviation network, the existing traffic prediction methods generally have the problem of low prediction accuracy. In order to overcome this problem, this paper makes full use of graph convolutional neural network and long—short memory network to construct a prediction system with short—term prediction ability. Specifically, this paper uses the graph convolutional neural network as a feature extraction tool to extract the key features of air traffic data, and solves the problem of long term and short term dependence between data through the long term memory network, then we build a high-precision air traffic prediction system based on it. Finally, we design a comparison experiment to compare the algorithm with the traditional algorithms. The results show that the algorithm we proposed in this paper has a higher accuracy in air flow prediction according to the lower loss function value.

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ED-ACNN: Novel attention convolutional neural network based on encoder–decoder framework for human traffic prediction

Applied Soft Computing ◽

10.1016/j.asoc.2020.106688 ◽

2020 ◽

Vol 97 ◽

pp. 106688

Author(s):

Bin Pu ◽

Yuan Liu ◽

Ningbo Zhu ◽

Kenli Li ◽

Keqin Li

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Traffic Prediction

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STCNN: A Spatio-Temporal Convolutional Neural Network for Long-Term Traffic Prediction

2019 20th IEEE International Conference on Mobile Data Management (MDM) ◽

10.1109/mdm.2019.00-53 ◽

2019 ◽

Cited By ~ 6

Author(s):

Zhixiang He ◽

Chi-Yin Chow ◽

Jia-Dong Zhang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Traffic Prediction ◽

Spatio Temporal

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Revisiting Random Forests in a Comparative Evaluation of Graph Convolutional Neural Network Variants for Traffic Prediction

10.1109/itsc48978.2021.9564595 ◽

2021 ◽

Author(s):

Ta Jiun Ting ◽

Xiaocan Li ◽

Scott Sanner ◽

Baher Abdulhai

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Random Forests ◽

Comparative Evaluation ◽

Traffic Prediction

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Time-Wise Attention Aided Convolutional Neural Network for Data-Driven Cellular Traffic Prediction

IEEE Wireless Communications Letters ◽

10.1109/lwc.2021.3078745 ◽

2021 ◽

pp. 1-1

Author(s):

Wenxin Shen ◽

Haixia Zhang ◽

Shuaishuai Guo ◽

Chuanting Zhang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Data Driven ◽

Traffic Prediction ◽

Cellular Traffic

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CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

10.1055/s-0040-1705059 ◽

2020 ◽

Author(s):

S Kashin ◽

D Zavyalov ◽

A Rusakov ◽

V Khryashchev ◽

A Lebedev

Keyword(s):

Neural Network ◽

Convolutional Neural Network

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No-Reference Utility Estimation with a Convolutional Neural Network

Electronic Imaging ◽

10.2352/issn.2470-1173.2018.09.iriacv-202 ◽

2018 ◽

Vol 2018 (9) ◽

pp. 202-1-202-6 ◽

Cited By ~ 2

Author(s):

Edward T. Scott ◽

Sheila S. Hemami

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Utility Estimation

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Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.10.ipas-180 ◽

2020 ◽

Vol 2020 (10) ◽

pp. 181-1-181-7

Author(s):

Takahiro Kudo ◽

Takanori Fujisawa ◽

Takuro Yamaguchi ◽

Masaaki Ikehara

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Architecture ◽

Large Scale ◽

Blind Deconvolution ◽

Training Dataset ◽

Image Deconvolution ◽

Classic Problem ◽

Key Points ◽

Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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