| 基于小波消噪的ARIMA与SVM组合交通流预测 |
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| 引用本文： | 谭满春,李英俊,徐建闽.基于小波消噪的ARIMA与SVM组合交通流预测[J].公路交通科技,2009,26(7). |
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| 作者姓名： | 谭满春 李英俊 徐建闽 |
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| 作者单位： | 1. 暨南大学,信息科学技术学院,广东,广州,510632
2. 华南理工大学,交通学院,广东,广州,510641 |
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| 基金项目： | 国家自然科学基金，广东省自然科学基金 |
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| 摘 要： | 针对实际交通系统时变复杂和变化的不确定性所带来的交通流量非线性和强干扰性的特征,首先应用小波分析方法,对原始交通数据进行了消噪处理,使消噪后的数据更能反映交通流的本质及变化规律;然后采用自回归求和滑动平均(ARIMA)和支持向量机(SVM)的结合预测模型对交通流进行了预测,最后用实测交通数据进行了验证分析,得到了两个结论:一是组合预测模型比单个预测模型的预测精度高;二是小波分析消噪后的组合预测模型比没有消噪的组合预测模型预测精度高.结果表明消噪后的组合预测模型具有较高的预测精度,可用于交通流的实时动态预测.
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| 关 键 词： | 智能运输系统 组合预测 小波消噪 支持向量机 ARIMA模型 交通流预测 |
A Hybrid ARIMA and SVM Model for Traffic Flow Prediction Based on Wavelet Denoising |
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| TAN Manchun,LI Yingjun,XU Jianmin.A Hybrid ARIMA and SVM Model for Traffic Flow Prediction Based on Wavelet Denoising[J].Journal of Highway and Transportation Research and Development,2009,26(7). |
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| Authors: | TAN Manchun LI Yingjun XU Jianmin |
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| Institution: | 1.School of Information Science and Technology;Jinan University;Guangzhou Guangdong 510632;China;2.School of Transportation;South China University of Technology;Guangzhou Guangdong 510641;China |
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| Abstract: | Based on the analysis of the characteristics of nonlinearity and strong interference of traffic flow due to the complex and uncertainty of time variance in real traffic system,a new approach was proposed for traffic flow prediction.First,wavelet transform is employed to eliminate the noise of original traffic data to reflect the essence and variation of traffic flow.Then a hybrid methodology that exploits the unique strength of the ARIMA model and the SVM model to forecast traffic flow with the worked data ... |
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| Keywords: | Intelligent Transport Systems combined prediction wavelet denoising support vector machine ARIMA model traffic flow prediction |
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