What is it about?
For solving the problem of traffic information accurately classification, via analyzing the characteristics of the multi-sourced traffic information and using redefined binary tree to overcome the shortcomings of the original SVM (Support Vector Machine) classification in information fusion, a multi-classification method using improved SVM in information fusion for traffic parameters forecasting is proposed. The experiment was conducted to examine the performance of the proposed scheme and the results reveal that the method can get more accurate and practical outcomes.
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Why is it important?
An improved multi-classification method is designed in SVM-based information fusion for traffic parameters forecasting, and our approach is formally validated using traffic parameters forecasting. Our protocol is also evaluated and compared with other methods. Our protocol leads to much smaller prediction errors and demonstrates its effectiveness from the results of those analyses.
Perspectives
For achieving better traffic parameters forecasting performance, this paper focuses on the problem of the multi-sourced traffic information classification in the process of information fusion. Through analysing the disadvantage of SVM and comparing different multi-classification methods of SVM, then improving SVM by binary tree, the proposed method can solve multi-classification problem in the information fusion process for better traffic parameters forecasting. To our best knowledge, there have been few applications of SVM in the field of analysing, dealing and utilizing traffic information. This study has provided an exploratory research on SVM’s application in traffic information classification and fusion and solved the typical multi-classification problems. Further studies on improving binary trees SVM for adaptively classifying all kinds of multi-source data and the selection of accurate parameters and kernel function model will be recommended in the future.
Hongzhuan ZHAO
Guilin University of Electronic Technology
Read the Original
This page is a summary of: A Multi-Classification Method of Improved SVM-based Information Fusion for Traffic Parameters Forecasting, PROMET - Traffic&Transportation, April 2016, Faculty of Transport and Traffic Sciences,
DOI: 10.7307/ptt.v28i2.1643.
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