Multilayer Perceptron and Particle Swarm Optimization Applied to Traffic Flow Prediction on Smart Cities

2019 ◽  
pp. 35-47 ◽  
Author(s):  
Lucas Rodrigues Frank ◽  
Yan Mendes Ferreira ◽  
Eduardo Pagani Julio ◽  
Francisco Henrique C. Ferreira ◽  
Bruno José Dembogurski ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Traffic Flow ◽  
Multilayer Perceptron ◽  
Smart Cities ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Traffic Flow Prediction ◽  
Flow Prediction

scholarly journals An AutoEncoder and LSTM-Based Traffic Flow Prediction Method

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2946 ◽  
Author(s):  
Wangyang Wei ◽  
Honghai Wu ◽  
Huadong Ma
Keyword(s):  
Traffic Flow ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Smart Cities ◽  
Prediction Method ◽  
Short Term ◽  
Term Memory ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Long Short Term Memory

Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.

scholarly journals Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1239
Author(s):  
Fatih Ecer ◽  
Sina Ardabili ◽  
Shahab S. Band ◽  
Amir Mosavi
Keyword(s):  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Population Size ◽  
Correlation Coefficient ◽  
Multilayer Perceptron ◽  
Gaussian Function ◽  
Particle Swarm ◽  
Series Data ◽  
Output Function ◽  
Swarm Optimization

Predicting stock market (SM) trends is an issue of great interest among researchers, investors and traders since the successful prediction of SMs’ direction may promise various benefits. Because of the fairly nonlinear nature of the historical data, accurate estimation of the SM direction is a rather challenging issue. The aim of this study is to present a novel machine learning (ML) model to forecast the movement of the Borsa Istanbul (BIST) 100 index. Modeling was performed by multilayer perceptron–genetic algorithms (MLP–GA) and multilayer perceptron–particle swarm optimization (MLP–PSO) in two scenarios considering Tanh (x) and the default Gaussian function as the output function. The historical financial time series data utilized in this research is from 1996 to 2020, consisting of nine technical indicators. Results are assessed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and correlation coefficient values to compare the accuracy and performance of the developed models. Based on the results, the involvement of the Tanh (x) as the output function, improved the accuracy of models compared with the default Gaussian function, significantly. MLP–PSO with population size 125, followed by MLP–GA with population size 50, provided higher accuracy for testing, reporting RMSE of 0.732583 and 0.733063, MAPE of 28.16%, 29.09% and correlation coefficient of 0.694 and 0.695, respectively. According to the results, using the hybrid ML method could successfully improve the prediction accuracy.

scholarly journals Urban Traffic Flow Prediction Model with CPSO/SSVM Algorithm under the Edge Computing Framework

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Fengkai Liu ◽  
Xingmin Ma ◽  
Xingshuo An ◽  
Guangnan Liang
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Traffic Flow ◽  
Urban Traffic ◽  
Support Vector ◽  
Swarm Optimization ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Chaotic Particle Swarm Optimization ◽  
Smooth Support Vector Machine

Urban traffic flow prediction has always been an important realm for smart city build-up. With the development of edge computing technology in recent years, the network edge nodes of smart cities are able to collect and process various types of urban traffic data in real time, which leads to the possibility of deploying intelligent traffic prediction technology with real-time analysis and timely feedback on the edge. In view of the strong nonlinear characteristics of urban traffic flow, multiple dynamic and static influencing factors involved, and increasing difficulty of short-term traffic flow prediction in a metropolitan area, this paper proposes an urban traffic flow prediction model based on chaotic particle swarm optimization algorithm-smooth support vector machine (CPSO/SSVM). The prediction model has built a new second-order smooth function to achieve better approximation and regression effects and has further improved the computational efficiency of the smooth support vector machine algorithm through chaotic particle swarm optimization. Simulation experiment results show that this model can accurately predict urban traffic flow.

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