[1]李林超,张健,杨帆,等.基于核函数切换和支持向量回归的交通量短时预测模型[J].东南大学学报(自然科学版),2017,47(5):1032-1036.[doi:10.3969/j.issn.1001-0505.2017.05.030]
 Li Linchao,Zhang Jian,Yang Fan,et al.Traffic volume prediction based on support vector regression with switch kernel functions[J].Journal of Southeast University (Natural Science Edition),2017,47(5):1032-1036.[doi:10.3969/j.issn.1001-0505.2017.05.030]
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基于核函数切换和支持向量回归的交通量短时预测模型()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
47
期数:
2017年第5期
页码:
1032-1036
栏目:
交通运输工程
出版日期:
2017-09-20

文章信息/Info

Title:
Traffic volume prediction based on support vector regression with switch kernel functions
作者:
李林超张健杨帆冉斌
东南大学交通学院, 南京 210096; 东南大学物联网交通应用研究中心, 南京 210096
Author(s):
Li Linchao Zhang Jian Yang Fan Ran Bin
School of Transportation, Southeast University, Nanjing 210096, China
Research Center for Internet of Mobility, Southeast University, Nanjing 210096, China
关键词:
交通运输系统工程 交通量 短时预测 支持向量回归 核函数
Keywords:
system engineering of communication and transportation traffic volume short-term prediction support vector regression kernel function
分类号:
U491
DOI:
10.3969/j.issn.1001-0505.2017.05.030
摘要:
基于高速公路交通量短时变化的非线性、不确定性和复杂性,利用支持向量回归模型,提出一种核函数切换的预测方法.首先,通过历史数据构建不同核函数的支持向量回归模型并对历史数据进行拟合,根据拟合的误差确定不同时刻对应的最优核函数类别; 然后根据历史数据及确定的不同时刻的核函数类别训练支持向量分类机; 最后利用支持向量分类机确定预测时刻最优的核函数类别,选取相应的支持向量回归模型进行预测.实例分析表明,与传统的支持向量回归模型相比,含核函数切换的预测方法预测精度较高,且具有较好的鲁棒性.
Abstract:
To simulate the nonlinear, probabilistic and complicated patterns in the short-term change of the highway traffic volume, a prediction model was proposed based on support vector regression and switch kernel functions. First, support vector regression models were built with different kernel functions by the historical data and the best kernel function was obtained using the fitting error. Then, a support vector machine model was trained. Finally, the best kernel function for the prediction interval was selected and the corresponding support vector regression model was implemented. A case study was used to evaluate the performance of the proposed model. The result shows that the model is superior to the traditional support vector regression model on the predicted accuracy, and thus it is more robust.

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备注/Memo

备注/Memo:
收稿日期: 2016-11-26.
作者简介: 李林超(1991—),男,博士生;冉斌(联系人),男,博士,教授,博士生导师,bran@seu.edu.cn.
基金项目: 交通运输部科技示范工程资助项目(2015364X16030, 2014364223150)、国家自然科学基金资助项目(6161001115)、东南大学优秀博士学位论文基金资助项目(YBJJ1736).
引用本文: 李林超,张健,杨帆,等.基于核函数切换和支持向量回归的交通量短时预测模型[J].东南大学学报(自然科学版),2017,47(5):1032-1036. DOI:10.3969/j.issn.1001-0505.2017.05.030.
更新日期/Last Update: 2017-09-20