Applying multiple kernel learning and support vector machine for solving the multicriteria and nonlinearity problems of traffic flow prediction
Applying multiple kernel learning and support vector machine for solving the multicriteria and nonlinearity problems of traffic flow prediction
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DOI：
10.1002/atr.1217
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发表时间：
2014-04
影响因子：
2.3
通讯作者：
Chenyun Yu;K. Lam
中科院分区：
文献类型：
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作者：
Chenyun Yu;K. Lam
本文提出了一种基于支持向量机和多核学习等核学习方法的交通流量预测模型。交通流预测是一个动态问题，具有多目标、非线性的复杂性质。首先对交通流的影响因素进行了研究，分别采用五点标度法和熵值法将定性因素转化为定量因素，并对这些因素进行排序。在此基础上，以影响因素和交通流量为输入输出变量，建立了基于支持向量机和MKL的交通流量预测模型。通过实例分析比较了MKL和支持向量机的预测能力。结果表明，支持向量机和MKL在预测精度和预测效率方面都有较好的效果，在适当的参数设置下，MKL的预测效果更好，准确率更高。因此，MKL可以提高交通流预测的决策能力。版权所有©2012 John Wiley&Sons，Ltd.
SUMMARY This article proposes to develop a prediction model for traffic flow using kernel learning methods such as support vector machine (SVM) and multiple kernel learning (MKL). Traffic flow prediction is a dynamic problem owing to its complex nature of multicriteria and nonlinearity. Influential factors of traffic flow were firstly investigated; five-point scale and entropy methods were employed to transfer the qualitative factors into quantitative ones and rank these factors, respectively. Then, SVM and MKL-based prediction models were developed, with the influential factors and the traffic flow as the input and output variables. The prediction capability of MKL was compared with SVM through a case study. It is proved that both the SVM and MKL perform well in prediction with regard to the accuracy rate and efficiency, and MKL is more preferable with a higher accuracy rate when under proper parameters setting. Therefore, MKL can enhance the decision-making of traffic flow prediction. Copyright © 2012 John Wiley & Sons, Ltd.