An LSTM-based Traffic Prediction Algorithm with Attention Mechanism for Satellite Network

2020 ◽  
Author(s):  
Feiyue Zhu ◽  
Lixiang Liu ◽  
Teng Lin
Keyword(s):  
Attention Mechanism ◽  
Prediction Algorithm ◽  
Traffic Prediction ◽  
Satellite Network

scholarly journals Research on GRU Neural Network Satellite Traffic Prediction Based on Transfer Learning

2021 ◽  
Author(s):  
Ning Li ◽  
Lang Hu ◽  
Zhong-Liang Deng ◽  
Tong Su ◽  
Jiang-Wang Liu
Keyword(s):  
Neural Network ◽  
Particle Filter ◽  
Transfer Learning ◽  
Network Traffic ◽  
Prediction Algorithm ◽  
Traffic Prediction ◽  
Satellite Network ◽  
Training Time ◽  
Gradient Descent Algorithm ◽  
Particle Filter Algorithm

AbstractIn this paper, we propose a Gated Recurrent Unit(GRU) neural network traffic prediction algorithm based on transfer learning. By introducing two gate structures, such as reset gate and update gate, the GRU neural network avoids the problems of gradient disappearance and gradient explosion. It can effectively represent the characteristics of long correlation traffic, and can realize the expression of nonlinear, self-similar, long correlation and other characteristics of satellite network traffic. The paper combines the transfer learning method to solve the problem of insufficient online traffic data and uses the particle filter online training algorithm to reduce the training time complexity and achieve accurate prediction of satellite network traffic. The simulation results show that the average relative error of the proposed traffic prediction algorithm is 35.80% and 8.13% lower than FARIMA and SVR, and the particle filter algorithm is 40% faster than the gradient descent algorithm.

scholarly journals An LSTM-Based Method with Attention Mechanism for Travel Time Prediction

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 861 ◽  
Author(s):  
Xiangdong Ran ◽  
Zhiguang Shan ◽  
Yufei Fang ◽  
Chuang Lin
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Traffic Prediction ◽  
Travel Time Prediction ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Departure Time ◽  
Long Short Term Memory

Traffic prediction is based on modeling the complex non-linear spatiotemporal traffic dynamics in road network. In recent years, Long Short-Term Memory has been applied to traffic prediction, achieving better performance. The existing Long Short-Term Memory methods for traffic prediction have two drawbacks: they do not use the departure time through the links for traffic prediction, and the way of modeling long-term dependence in time series is not direct in terms of traffic prediction. Attention mechanism is implemented by constructing a neural network according to its task and has recently demonstrated success in a wide range of tasks. In this paper, we propose an Long Short-Term Memory-based method with attention mechanism for travel time prediction. We present the proposed model in a tree structure. The proposed model substitutes a tree structure with attention mechanism for the unfold way of standard Long Short-Term Memory to construct the depth of Long Short-Term Memory and modeling long-term dependence. The attention mechanism is over the output layer of each Long Short-Term Memory unit. The departure time is used as the aspect of the attention mechanism and the attention mechanism integrates departure time into the proposed model. We use AdaGrad method for training the proposed model. Based on the datasets provided by Highways England, the experimental results show that the proposed model can achieve better accuracy than the Long Short-Term Memory and other baseline methods. The case study suggests that the departure time is effectively employed by using attention mechanism.

scholarly journals Distributed Routing Strategy Based on Machine Learning for LEO Satellite Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Zhenyu Na ◽  
Zheng Pan ◽  
Xin Liu ◽  
Zhian Deng ◽  
Zihe Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Low Earth Orbit ◽  
Traffic Prediction ◽  
Satellite Network ◽  
Integrated Networks ◽  
Distributed Routing ◽  
Learning Machine ◽  
Terrestrial Communications ◽  
Leo Satellite

As the indispensable supplement of terrestrial communications, Low Earth Orbit (LEO) satellite network is the crucial part in future space-terrestrial integrated networks because of its unique advantages. However, the effective and reliable routing for LEO satellite network is an intractable task due to time-varying topology, frequent link handover, and imbalanced communication load. An Extreme Learning Machine (ELM) based distributed routing (ELMDR) strategy was put forward in this paper. Considering the traffic distribution density on the surface of the earth, ELMDR strategy makes routing decision based on traffic prediction. For traffic prediction, ELM, which is a fast and efficient machine learning algorithm, is adopted to forecast the traffic at satellite node. For the routing decision, mobile agents (MAs) are introduced to simultaneously and independently search for LEO satellite network and determine routing information. Simulation results demonstrate that, in comparison to the conventional Ant Colony Optimization (ACO) algorithm, ELMDR not only sufficiently uses underutilized link, but also reduces delay.

scholarly journals A Convolution Component-Based Method with Attention Mechanism for Travel-Time Prediction

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2063 ◽  
Author(s):  
Xiangdong Ran ◽  
Zhiguang Shan ◽  
Yufei Fang ◽  
Chuang Lin
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Predictive Accuracy ◽  
Attention Mechanism ◽  
Traffic Prediction ◽  
Time Interval ◽  
Learning Approaches ◽  
Traffic Data ◽  
Travel Time Prediction ◽  
Time Intervals

Deep learning approaches have been recently applied to traffic prediction because of their ability to extract features of traffic data. While convolutional neural networks may improve the predictive accuracy by transiting traffic data to images and extracting features in the images, the convolutional results can be improved by using the global-level representation that is a direct way to extract features. The time intervals are not considered as aspects of convolutional neural networks for traffic prediction. The attention mechanism may adaptively select a sequence of regions and only process the selected regions to better extract features when aspects are considered. In this paper, we propose the attention mechanism over the convolutional result for traffic prediction. The proposed method is based on multiple links. The time interval is considered as the aspect of attention mechanism. Based on the dataset provided by Highways England, the experimental results show that the proposed method can achieve better accuracy than the baseline methods.

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