基于奇异谱分析和CKF-LSSVM的短时交通流量预测

doi:10.13229/j.cnki.jdxbgxb201606006

吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (6): 1792-1798.doi: 10.13229/j.cnki.jdxbgxb201606006

基于奇异谱分析和CKF-LSSVM的短时交通流量预测

商强¹, 杨兆升^{1, 2, 3}, 张伟^{1, 2, 4}, 邴其春¹, 周熙阳¹

1.吉林大学交通学院,长春 130022;
2.吉林大学汽车仿真与控制国家重点实验室,长春 130022;
3.吉林大学吉林省道路交通重点实验室,长春 130022;
4.山东高速公路股份有限公司,济南 250014

收稿日期:2015-11-23 出版日期:2016-11-20 发布日期:2016-11-20
通讯作者: 张伟(1978-),男,高级工程师,在站博士后.研究方向:智能交通系统理论与技术.E-mail:z_wei@126.com
作者简介:商强(1987-),男,博士研究生.研究方向:智能交通系统理论与技术.
基金资助:
国家科技支撑计划项目(2014BAG03B03); 国家自然科学基金项目(51308248, 51408257, 51308249); 山东省省管企业科技创新项目(20122150251-1)

Short-term traffic flow prediction based on singular spectrum analysis and CKF-LSSVM

SHANG Qiang¹, YANG Zhao-sheng^{1, 2, 3}, ZHANG Wei^{1, 2, 4}, Bing Qi-chun¹, ZHOU Xi-yang¹

1.College of Transportation, Jilin University,Changchun 130022,China;
2.State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130022, China;
3.Jilin Province Key Laboratory of Road Traffic,Jilin University, Changchun 130022,China;
4.Shandong High-speed Co.,Ltd.,Ji'nan 250014,China

Received:2015-11-23 Online:2016-11-20 Published:2016-11-20

摘要/Abstract

摘要： 为了提高短时交通流预测的精度,提出了基于奇异谱分析和组合核函数最小二乘支持向量机(CKF-LSSVM)的短时交通流预测模型。首先,采用奇异谱分析方法,滤除交通流序列的噪声成分。然后,使用降噪后的交通流数据训练CKF-LSSVM,并通过粒子群优化算法确定模型参数。最后,以厦门市的实测数据为基础,对预测模型进行实验验证和对比分析。结果表明:本文所构建模型具有较好的预测效果,能够有效提高短时交通流预测精度。

关键词: 交通运输系统工程, 短时交通流预测, 奇异谱分析, 支持向量机, 组合核函数

Abstract: In order to improve the accuracy of short-time traffic flow prediction, a short-term traffic flow prediction model based on singular spectrum analysis and Combined Kernel Function (CKF) Least Square Support Vector Machine (LSSVM) is proposed. Singular spectrum analysis technology is used to filter out the noise of traffic time series. Then, the processed traffic flow data are used to train the CKF-LSSVM, and the parameters of the model are determined by particle swarm optimization algorithm. Finally, validation and comparative analysis of the model are carried out using the measured in Xiamen, China. Experimental results indicate that the proposed model has good prediction performance and can effectively improve the accuracy of short-time traffic flow prediction.

Key words: engineering of communication and transportation system, short-term traffic flow prediction, singular spectrum analysis, support vector machine, combined kernel function

中图分类号:

U491

商强, 杨兆升, 张伟, 邴其春, 周熙阳. 基于奇异谱分析和CKF-LSSVM的短时交通流量预测[J]. 吉林大学学报(工学版), 2016, 46(6): 1792-1798.

SHANG Qiang, YANG Zhao-sheng, ZHANG Wei, Bing Qi-chun, ZHOU Xi-yang. Short-term traffic flow prediction based on singular spectrum analysis and CKF-LSSVM[J]. 吉林大学学报(工学版), 2016, 46(6): 1792-1798.

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基于奇异谱分析和CKF-LSSVM的短时交通流量预测

Short-term traffic flow prediction based on singular spectrum analysis and CKF-LSSVM

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