Abstract
In order to reduce the impact of traffic incidents on traffic operation, an Automated Traffic Incidents Detection (SVM-AID) algorithm based on Support Vector Machine (SVM) is proposed. This algorithm is of great significance for improving the efficiency of traffic management and improving the effect of traffic management. This article first introduces the background of the topic selection of the Traffic Incidents Detection algorithm, the research status at home and abroad. Then it focuses on the Optimal Separating Hyperplane, linear separable SVM, linear inseparable SVM, nonlinear separable SVM, and commonly used kernel functions. Then, the design flow chart based on the SVM-AID algorithm is given, and the principle component analysis method, Normalization Method and the selection method of Support Vector Machine parameters are introduced. Finally, using the processed data, 4 experiments were designed to test the classification performance of the SVM-AID algorithm, and the influence of each parameter in the SVM on the classification effect was analyzed. The results of the final experiment also showed us the design The effectiveness of the SVM-AID algorithm.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China, grant number 72073041. Open Foundation for the University Innovation Platform in the Hunan Province, grant number 18K103.2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property.
Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology. 2020 Hunan Provincial Higher Education Teaching Reform Research Project under Grant HNJG-2020–1130, HNJG-2020–1124.2020 General Project of Hunan Social Science Fund under Grant 20B16.
Scientific Research Project of Education Department of Hunan Province (Grand No. 20K021), Social Science Foundation of Hunan Province (Grant No. 17YBA049).
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Zhang, R., Huang, J., Yan, Y., Gao, Y. (2021). Research on Support Vector Machine in Traffic Detection Algorithm. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2021. Communications in Computer and Information Science, vol 1423. Springer, Cham. https://doi.org/10.1007/978-3-030-78618-2_18
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DOI: https://doi.org/10.1007/978-3-030-78618-2_18
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