Cellular Network Based Real-Time Urban Road Traffic State Estimation Framework Using Neural Network Model Estimation

2015 ◽  
Author(s):  
Ayalew Belay Habtie ◽  
Ajith Abraham ◽  
Dida Midekso
Keyword(s):  
Neural Network ◽  
State Estimation ◽  
Real Time ◽  
Network Model ◽  
Neural Network Model ◽  
Cellular Network ◽  
Road Traffic ◽  
Model Estimation ◽  
Urban Road ◽  
Traffic State

scholarly journals Attention based multi-component spatiotemporal cross-domain neural network model for wireless cellular network traffic prediction

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Qingtian Zeng ◽  
Qiang Sun ◽  
Geng Chen ◽  
Hua Duan
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Traffic ◽  
Cellular Network ◽  
Critical Issue ◽  
Traffic Prediction ◽  
Weekly Cycle ◽  
Cross Domain ◽  
Cellular Traffic

AbstractWireless cellular traffic prediction is a critical issue for researchers and practitioners in the 5G/B5G field. However, it is very challenging since the wireless cellular traffic usually shows high nonlinearities and complex patterns. Most existing wireless cellular traffic prediction methods lack the abilities of modeling the dynamic spatial–temporal correlations of wireless cellular traffic data, thus cannot yield satisfactory prediction results. In order to improve the accuracy of 5G/B5G cellular network traffic prediction, an attention-based multi-component spatiotemporal cross-domain neural network model (att-MCSTCNet) is proposed, which uses Conv-LSTM or Conv-GRU for neighbor data, daily cycle data, and weekly cycle data modeling, and then assigns different weights to the three kinds of feature data through the attention layer, improves their feature extraction ability, and suppresses the feature information that interferes with the prediction time. Finally, the model is combined with timestamp feature embedding, multiple cross-domain data fusion, and jointly with other models to assist the model in traffic prediction. Experimental results show that compared with the existing models, the prediction performance of the proposed model is better. Among them, the RMSE performance of the att-MCSTCNet (Conv-LSTM) model on Sms, Call, and Internet datasets is improved by 13.70 ~ 54.96%, 10.50 ~ 28.15%, and 35.85 ~ 100.23%, respectively, compared with other existing models. The RMSE performance of the att-MCSTCNet (Conv-GRU) model on Sms, Call, and Internet datasets is about 14.56 ~ 55.82%, 12.24 ~ 29.89%, and 38.79 ~ 103.17% higher than other existing models, respectively.

GIS aided sustainable urban road management with a unifying queueing and neural network model

2021 ◽  
Vol 291 ◽  
pp. 116818
Author(s):  
Huibo Bi ◽  
Wen-Long Shang ◽  
Yanyan Chen ◽  
Kezhi Wang ◽  
Qing Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Urban Road ◽  
Road Management

A conceptual and neural network model for real-time flood forecasting of the Tiber River in Rome

2010 ◽  
Vol 35 (3-5) ◽  
pp. 187-194 ◽  
Author(s):  
G. Napolitano ◽  
L. See ◽  
B. Calvo ◽  
F. Savi ◽  
A. Heppenstall
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Model ◽  
Neural Network Model ◽  
Flood Forecasting ◽  
Tiber River

scholarly journals Real-Time Optimization and Control of Nonlinear Processes Using Machine Learning

Mathematics ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 890 ◽  
Author(s):  
Zhihao Zhang ◽  
Zhe Wu ◽  
David Rincon ◽  
Panagiotis Christofides
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Real Time ◽  
Network Model ◽  
Neural Network Model ◽  
First Principles ◽  
Chemical Process ◽  
Feed Forward Neural Network ◽  
Time Optimization ◽  
Real Time Optimization

Machine learning has attracted extensive interest in the process engineering field, due to the capability of modeling complex nonlinear process behavior. This work presents a method for combining neural network models with first-principles models in real-time optimization (RTO) and model predictive control (MPC) and demonstrates the application to two chemical process examples. First, the proposed methodology that integrates a neural network model and a first-principles model in the optimization problems of RTO and MPC is discussed. Then, two chemical process examples are presented. In the first example, a continuous stirred tank reactor (CSTR) with a reversible exothermic reaction is studied. A feed-forward neural network model is used to approximate the nonlinear reaction rate and is combined with a first-principles model in RTO and MPC. An RTO is designed to find the optimal reactor operating condition balancing energy cost and reactant conversion, and an MPC is designed to drive the process to the optimal operating condition. A variation in energy price is introduced to demonstrate that the developed RTO scheme is able to minimize operation cost and yields a closed-loop performance that is very close to the one attained by RTO/MPC using the first-principles model. In the second example, a distillation column is used to demonstrate an industrial application of the use of machine learning to model nonlinearities in RTO. A feed-forward neural network is first built to obtain the phase equilibrium properties and then combined with a first-principles model in RTO, which is designed to maximize the operation profit and calculate optimal set-points for the controllers. A variation in feed concentration is introduced to demonstrate that the developed RTO scheme can increase operation profit for all considered conditions.

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