Dynamic bandwidth management based on traffic prediction using Deep Long Short Term Memory

2016 ◽  
Author(s):  
Tjeng Wawan Cenggoro ◽  
Ida Siahaan
Keyword(s):  
Short Term Memory ◽  
Traffic Prediction ◽  
Short Term ◽  
Bandwidth Management ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Dynamic Bandwidth

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.

DEEP LEARNING WITH LONG-TERM SHORT-TERM MEMORY FOR IOT TRAFFIC PREDICTION

2021 ◽  
Author(s):  
А.С. БОРОДИН ◽  
А.Р. АБДЕЛЛАХ ◽  
А.Е. КУЧЕРЯВЫЙ
Keyword(s):  
Deep Learning ◽  
Communication Networks ◽  
Short Term Memory ◽  
Traffic Prediction ◽  
Short Term ◽  
Term Memory ◽  
Traffic Forecasting ◽  
Long Short Term Memory ◽  
Iot Devices

Использование искусственного интеллекта в сетях связи пятого (5G) и последующих поколений дает новые возможности, в том числе для прогнозирования трафика. Это особенно важно для трафика интернета вещей (IoT - Internet of Things), поскольку число устройств IoT очень велико. Предлагается для прогнозирования трафика IoT применить глубокое обучение с использованием нейронной сети долговременной краткосрочной памяти LSTM (Long Short-Term Memory). The use of artificial intelligence in communication networks of the 5G and subsequent generations provides completely new opportunities, including for traffic forecasting. This is especially important for IoT traffic because the number of IoT devices is very large. The article proposes to apply deep learning to predict IoT traffic using a neural network of longterm short-term memory (LSTM).

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