| 改进的支持向量机算法在短时交通流预测中的应用 |
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| 引用本文： | 唐世星.改进的支持向量机算法在短时交通流预测中的应用[J].承德石油高等专科学校学报,2012(1):34-36. |
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| 作者姓名： | 唐世星 |
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| 作者单位： | 承德石油高等专科学校人事处,河北承德067000 |
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| 基金项目： | 河北省高等学校科学技术研究指导项目：Z2010210 |
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| 摘 要： | 把交叉验证和网格搜索算法引入支持向量机预测算法,建立了改进的支持向量机预测模型,并将其应用于短时交通流预测进行实证分析。以某城市道路的实时数据来对模型进行验证,预测结果表明了该模型的有效性。
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| 关 键 词： | 短时交通流预测 交叉验证 网格搜索 惩罚因子 |
Application of Improved Support Vector Machine Algorithm in Short-term Traffic Flow Forecast |
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| TANG Shi-xing.Application of Improved Support Vector Machine Algorithm in Short-term Traffic Flow Forecast[J].Journal of Chengde Petroleum College,2012(1):34-36. |
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| Authors: | TANG Shi-xing |
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| Affiliation: | TANG Shi-xing(Personnel Department,Chengde Petroleum College,Chengde 067000,Hebei,China) |
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| Abstract: | The paper introduces cross-validation and grid-search method to optimize the prediction accuracy of Support Vector Machine models,the establishment of an improved Support Vector Machine prediction model,and applied to short-term traffic flow forecasting empirical analysis.The paper also uses the real time data of certain urban road to test the efficiency of the proposed model and the result is satisfactory. |
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| Keywords: | short-term traffic flow forecasting cross-validation grid-search method penalty factor |
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