Vessel traffic flow forecasting with the combined model based on support vector machine

2015 ◽  
Author(s):  
Wang Haiyan ◽  
Wang Youzhen
Keyword(s):  
Support Vector Machine ◽  
Traffic Flow ◽  
Support Vector ◽  
Combined Model ◽  
Model Based ◽  
Traffic Flow Forecasting

scholarly journals Short-Term Traffic Flow Forecasting Based on Data-Driven Model

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 152 ◽  
Author(s):  
Su-qi Zhang ◽  
Kuo-Ping Lin
Keyword(s):  
Support Vector Machine ◽  
Traffic Flow ◽  
Extreme Learning Machine ◽  
Support Vector ◽  
Local Optimum ◽  
Short Term ◽  
Swarm Optimization ◽  
Traffic Flow Forecasting ◽  
Learning Machine ◽  
The One

Short-term traffic flow forecasting is the technical basis of the intelligent transportation system (ITS). Higher precision, short-term traffic flow forecasting plays an important role in alleviating road congestion and improving traffic management efficiency. In order to improve the accuracy of short-term traffic flow forecasting, an improved bird swarm optimizer (IBSA) is used to optimize the random parameters of the extreme learning machine (ELM). In addition, the improved bird swarm optimization extreme learning machine (IBSAELM) model is established to predict short-term traffic flow. The main researches in this paper are as follows: (1) The bird swarm optimizer (BSA) is prone to fall into the local optimum, so the distribution mechanism of the BSA optimizer is improved. The first five percent of the particles with better fitness values are selected as producers. The last ten percent of the particles with worse fitness values are selected as beggars. (2) The one-day and two-day traffic flows are predicted by the support vector machine (SVM), particle swarm optimization support vector machine (PSOSVM), bird swarm optimization extreme learning machine (BSAELM) and IBSAELM models, respectively. (3) The prediction results of the models are evaluated. For the one-day traffic flow sequence, the mean absolute percentage error (MAPE) values of the IBSAELM model are smaller than the SVM, PSOSVM and BSAELM models, respectively. The experimental analysis results show that the IBSAELM model proposed in this study can meet the actual engineering requirements.

A combined model based on feature selection and support vector machine for PM2.5 prediction

2021 ◽  
pp. 1-15
Author(s):  
Xiaocong Lai ◽  
Hua Li ◽  
Ying Pan
Keyword(s):  
Air Pollution ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Air Quality ◽  
Prediction Model ◽  
Meteorological Factors ◽  
Feature Selection Method ◽  
Support Vector ◽  
Combined Model ◽  
Model Based

With the increasing attention to the environment and air quality, PM2.5 has been paid more and more attention. It is expected to excavate useful information in meteorological data to predict air pollution, however, the air quality is greatly affected by meteorological factors, and how to establish an effective air quality prediction model has always been a problem that people urgently need to solve. This paper proposed a combined model based on feature selection and Support Vector Machine (SVM) for PM2.5 prediction. Firstly, aiming at the influence of meteorological factors on PM2.5, a feature selection method based on linear causality is proposed to find out the causality between features and select the features with strong causality, so as to remove the redundant features in air pollution data and reduce the workload of data analysis. Then, a method based on SVM is proposed to analyze and solve the nonlinear problems in the data, for reducing the prediction error, a method of particle swarm optimization is also used to optimize SVM parameters. Finally, the above methods are combined into a prediction model, which is suitable for the current air pollution control. 12 representative data sets on the UCI (University of California, Irvine) website are used to verify the combined model, and the experimental results show that the model is feasible and effective.

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