scholarly journals Application of Artificial Neural Networks and Singular-Spectral Analysis in Forecasting the Daily Traffic in the Moscow Metro

2018 ◽  
Vol 173 ◽  
pp. 05009
Author(s):  
Victor Ivanov ◽  
Evgenii Osetrov
Keyword(s):  
Neural Networks ◽  
Spectral Analysis ◽  
Artificial Neural Networks ◽  
Reasonable Accuracy ◽  
Medium Term ◽  
Passenger Traffic ◽  
Singular Spectral Analysis ◽  
Traffic Volumes ◽  
Artificial Neural

In this paper, we investigate the possibility of applying various approaches to solving the problem of medium-term forecasting of daily passenger traffic volumes in the Moscow metro (MM): 1) on the basis of artificial neural networks (ANN); 2) using the singular-spectral analysis implemented in the package “Caterpillar”-SSA; 3) sharing the ANN and the “Caterpillar”-SSA approach. We demonstrate that the developed methods and algorithms allow us to conduct medium-term forecasting of passenger traffic in the MM with reasonable accuracy.

Modular Artificial Neural Networks for Solving the Inverse Transportation Planning Problem

2003 ◽  
Vol 1836 (1) ◽  
pp. 37-44 ◽  
Author(s):  
Adel W. Sadek ◽  
Charles Mark
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Transportation Planning ◽  
Current System ◽  
Transportation Network ◽  
Network Size ◽  
Training Data ◽  
Planning Problem ◽  
Traffic Volumes ◽  
Artificial Neural

Because major capacity-expansion projects are very unlikely in the coming years, transportation planners need to view the existing infrastructure as fixed and to start thinking about how much development the current system can sustain. This line of thinking, which involves deriving land use limits from infrastructure capacity, requires solving the inverse of the typical transportation planning problem. Modular artificial neural networks (ANNs) were developed for solving the inverse transportation planning problem. ANNs were designed to predict zonal trip ends, given the traffic volumes on the links of the transportation network. Computational experiments were performed to study the effect on ANN accuracy of three factors: transportation network size, variability in training data, and ANN topology. ANNs were shown to be quite capable of capturing the relationship between link volumes and zonal trip ends for both small and medium-sized transportation networks and for degrees of variability in the training data. Modular ANNs with one or two hidden layers appeared to outperform other ANN topologies.

Prediction by using spectral analysis and artificial neural networks methodologies: Comparison Study

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Mohammed Habib Al- Sharoot ◽  
Emaan Yousif Abdoon
Keyword(s):  
Neural Networks ◽  
Spectral Analysis ◽  
Artificial Neural Networks ◽  
Exchange Rate ◽  
Absolute Error ◽  
Percentage Error ◽  
The Mean ◽  
Us Dollar ◽  
Artificial Neural ◽  
Stable Economy

The variations in exchange rate, especially the sudden unexpected increases and decreases, have significant impact on the national economy of any country. Iraq is no exception; therefore, the accurate forecasting of exchange rate of Iraqi dinar to US dollar plays an important role in the planning and decision-making processes as well as the maintenance of a stable economy in Iraq. This research aims to compare spectral analysis methodology to artificial neural networks in terms of forecasting the exchange rate of Iraqi dinar to US dollar based on data provided by the Iraqi Central Bank for the period 30/01/2004 and 30/12/2014. Based on the Mean Square Error (MSE), the Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE) as criteria to compare the two methodologies, it was concluded that is artificial neural networks better than spectral analysis approach in forecasting.

FORECASTING ELECTRIC CONSUMPTION OF THE ENTERPRISE USING ARTIFICIAL NEURAL NETWORKS

2021 ◽  
Vol 7 (1) ◽  
pp. 177-193
Author(s):  
Sameh A. Kassem ◽  
Abdulla H. A. EBRAHIM ◽  
Abdulla M. Khasan ◽  
Alla G. Logacheva
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Power Consumption ◽  
Data Driven ◽  
Past Century ◽  
Medium Term ◽  
The Past ◽  
Artificial Neural ◽  
Feedback Algorithm

Energy consumption has increased dramatically over the past century due to many factors, including both technological, social and economic factors. Therefore, predicting energy consumption is of great importance for many parameters, including planning, management, optimization and conservation. Data-driven models for predicting energy consumption have grown significantly over the past several decades due to their improved performance, reliability, and ease of deployment. Artificial neural networks are among the most popular data-driven approaches among the many different types of models today. This article discusses the possibility of using artificial neural networks for medium-term forecasting of the power consumption of an enterprise. The task of constructing an artificial neural network using a feedback algorithm for training a network based on the Matlab mathematical package has been implemented. The authors have analyzed such characteristics as parameter setting, implementation complexity, learning rate, convergence of the result, forecasting accuracy, and stability. The results obtained led to the conclusion that the feedback algorithm is well suited for medium-term forecasting of power consumption.

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