Short-term traffic prediction under disruptions using deep learning

2020 ◽  
pp. 79-114
Keyword(s):  
Deep Learning ◽  
Traffic Prediction ◽  
Short Term

An Improved Bayesian Combination Model for Short-Term Traffic Prediction With Deep Learning

2020 ◽  
Vol 21 (3) ◽  
pp. 1332-1342 ◽  
Author(s):  
Yuanli Gu ◽  
Wenqi Lu ◽  
Xinyue Xu ◽  
Lingqiao Qin ◽  
Zhuangzhuang Shao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Traffic Prediction ◽  
Short Term ◽  
Combination Model

scholarly journals LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks

2020 ◽  
Author(s):  
Rongzhou Huang ◽  
Chuyin Huang ◽  
Yubao Liu ◽  
Genan Dai ◽  
Weiyang Kong
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Traffic Management ◽  
Route Planning ◽  
Traffic Prediction ◽  
Learning Approaches ◽  
Short Term ◽  
Temporal Prediction ◽  
Convolutional Networks ◽  
Short Term Prediction

Traffic prediction is a classical spatial-temporal prediction problem with many real-world applications such as intelligent route planning, dynamic traffic management, and smart location-based applications. Due to the high nonlinearity and complexity of traffic data, deep learning approaches have attracted much interest in recent years. However, few methods are satisfied with both long and short-term prediction tasks. Target at the shortcomings of existing studies, in this paper, we propose a novel deep learning framework called Long Short-term Graph Convolutional Networks (LSGCN) to tackle both traffic prediction tasks. In our framework, we propose a new graph attention network called cosAtt, and integrate both cosAtt and graph convolution networks (GCN) into a spatial gated block. By the spatial gated block and gated linear units convolution (GLU), LSGCN can efficiently capture complex spatial-temporal features and obtain stable prediction results. Experiments with three real-world traffic datasets verify the effectiveness of LSGCN.

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).

Short-Term Traffic Prediction Based on Deep Learning

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Ming-Xia Huang ◽  
Wen-Tao Li ◽  
Lu Wang ◽  
Shan-Shan Fan
Keyword(s):  
Deep Learning ◽  
Traffic Prediction ◽  
Short Term

Very short-term forecasting of wind power generation using hybrid deep learning model

2021 ◽  
Vol 296 ◽  
pp. 126564
Author(s):  
Md Alamgir Hossain ◽  
Ripon K. Chakrabortty ◽  
Sondoss Elsawah ◽  
Michael J. Ryan
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Power ◽  
Learning Model ◽  
Wind Power Generation ◽  
Short Term ◽  
Short Term Forecasting ◽  
Deep Learning Model
Sign in / Sign up

Export Citation Format

Share Document