基于ADPSO算法优化LSSVM的高速公路交通量预测方法

doi:10.3969/j.issn.1000-1565.2017.03.013

河北大学学报(自然科学版) ›› 2017, Vol. 37 ›› Issue (3): 302-308.DOI: 10.3969/j.issn.1000-1565.2017.03.013

基于ADPSO算法优化LSSVM的高速公路交通量预测方法

司文静¹,封喜波²,耿立艳^3,4,张占福⁵

出版日期:2017-05-25 发布日期:2017-05-25

A forecasting method of highway traffic flow using LSSVM optimized by ADPSO algorithm

SI Wenjing¹,FENG Xibo²,GENG Liyan^3,4,ZHANG Zhanfu⁵

1.Construction Engineering Department, North China Institute of Aerospace Engineering, Langfang 065000, China; 2.Hebei Province Expressway Langfang Beisanxian CountyManagement Department, Langfang 065000, China; 3.School of Economics and Management, Shijiazhuang Tiedao University, Shijiazhuang 050043, China; 4.Business School, ManchesterMetropolitan University, Manchester, M15 6BH, UK; 5.Sifang College, Shijiazhuang TiedaoUniversity, Shijiazhuang 051132, China

Online:2017-05-25 Published:2017-05-25

摘要/Abstract

摘要： 针对高速公路交通量与其经济影响因素之间的复杂非线性关系,将最小二乘支持向量机(least squares support vector machines,LSSVM)与自适应动态粒子群优化(adaptive dynamic particle swarm optimization,ADPSO)算法相结合,提出一种ADPSO算法优化LSSVM的高速公路交通量新型预测方法.将建模简单、精度高的LSSVM作为预测模型,通过寻优能力优异的ADPSO算法选择LSSVM最优参数.以某市高速公路交通量为例验证模型的有效性.结果表明,所提方法的预测性能较好,适合于高速公路交通量的短期预测.

关键词: 高速公路, 交通量预测, 自适应动态粒子群优化算法, 最小二乘支持向量机

Abstract: There is a complex nonlinear relationship between highway traffic flow and its influencing factors.Combing least squares support vector machines(LSSVM)with adaptive dynamic particle swarm- optimization(ADPSO)algorithm,this paper proposed a new highway traffic flow forecasting method based on LSSVM optimized by ADPSO algorithm.Highway traffic flow was forecasted by LSSVM with the advantages of easy modeling and high precision.And the optimal parameters of LSSVM were selected based on the good optimization ability of ADPSO algorithm.An example analysis on the highway traffic flow in a city was performed to test the effectiveness of LSSVM-ADPSO model.The results indicate that the proposed method has better highway traffic flow forecasting performance and is suitable for short-term highway traffic flow forecasting.

Key words: highway, traffic flow forecasting, adaptive dynamic particle swarm optimization algorithm, least squares support vector machines

中图分类号:

U491.14

司文静,封喜波,耿立艳,张占福. 基于ADPSO算法优化LSSVM的高速公路交通量预测方法[J]. 河北大学学报(自然科学版), 2017, 37(3): 302-308.

SI Wenjing,FENG Xibo,GENG Liyan,ZHANG Zhanfu. A forecasting method of highway traffic flow using LSSVM optimized by ADPSO algorithm[J]. Journal of Hebei University (Natural Science Edition), 2017, 37(3): 302-308.

参考文献

[1] 徐建闽,尹宏宾.广珠高速公路交通量预测[J].系统工程理论与实践,1998,18(4):122-125.DOI:10.3321/j.issn:1000-6788.1998.04.021. XU J M,YIN H B.Guangzhou-Zhuhai expressway traffic volume forecast[J].Systems Engineering-Theory & Practice,1998,18(4):122-125.DOI:10.3321/j.issn:1000-6788.1998.04.021.
[2] 朱顺应,王红,李关寿.路段上短时间区段内交通量预测ARIMA模型[J].重庆交通大学学报(自然科学版),2003,22(1):76-77,95.DOI:10.3969/j.issn.1674-0696.2003.01.019. ZHU S Y,WANG H,LI G S.The ARIMA model used in forecasting of traffic volume in short interval on the link[J].Journal of Chongqing Jiaotong University,2003,22(1):76-77,95.DOI:10.3969/j.issn.1674-0696.2003.01.019.
[3] 高连生,易诞,毛娜,等.基于改进的GM(1,1)的长期交通量预测模型[J].铁道科学与工程学报,2015,12(1):203-207.DOI:10.3969/j.issn.1672-7029.2015.01.033. GAO L S,YI D,MAO N,LI L.A forecast model for long -term traffic volu me based on improved GM(1,1)model[J].Journal of Railway Science and Engineering,2015,12(1):203-207.DOI:10.3969/j.issn.1672-7029.2015.01.033.
[4] 王炎,王华.基于BP-NN和遗传算法的高速公路交通量预测[J].计算机工程与应用,2006(4):226-228.DOI:10.3321/j.issn:1002-8331.2006.04.070. WANG Y,WANG H.Prediction of highway traffic based on BP-neural network and genetic algorithms[J].Computer Engineering and Applications,2006(4):226-228.DOI:10.3321/j.issn:1002-8331.2006.04.070.
[5] VAPNIK V N.An overview of statistical learning theory[J].IEEE Transactions on Neural Networks,1999,l0(5):988-999.DOI:10.1109/72.788640.
[6] HU W B,YAN L P,LIU K Z,et al.A Short-term traffic flow forecasting method based on the hybrid PSO-SVR[J].Neural Processing Letters,2016,43(1):155-172.DOI:10.1007/s11063-015-9409-6.
[7] AHN J,KO E,KIM E Y.Highway traffic flow prediction using support vector regression and Bayesian classifier[Z].The IEEE International Conference on Big Data and Smart Computing(BigComp),HongKong,2016.DOI:10.1109/BIGCOMP.2016.7425919.
[8] SUYKENS J A K,VANDEVALLE J.Least squares support vector machine classifiers[J].Neural Processing Letters,1999,9(3):293-300.DOI:10.1023/A:1018628609742.
[9] 戢小辉.基于灰色关联的LS-SVM道路交通事故预测[J].计算机应用研究,2016,33(3):806-809.DOI:10.3969/j.issn.1001-3695.2016.03.037. JI X H.Forecast model of road traffic accidents based on LS-SVMwith grey correlation analysis[J].Application Research of Computers,2016,33(3):806-809.DOI:10.3969/j.issn.1001-3695.2016.03.037.
[10] 邓佳佳,刘爽.基于免疫模糊聚类的LSSVM在短期负荷预测中的应用[J].河北大学学报(自然科学版),2012,32(3):234-239.DOI:10.3969/j.issn.1000-1565.2012.03.003. DENG J J,LIU S.Application of LSSVM based on immune fuzzy clustering algorithm to short-term load forecasting[J].Journal of Hebei University(Natural Science Edition),2012,32(3):234-239.DOI:10.3969/j.issn.1000-1565.2012.03.003.
[11] POLI R,KENNEDY J,BLACKWELL T.Particle swarm optimization:An overview[J].Swarm Intelligence,2007,1(1):33-57.DOI:10.1007/s11721-007-0002-0.
[12] SHI Y,EBERHART R C.Empirical study of particle swarm optimization[Z].The IEEE International Congress on Evolutionary Computation,Piscataway,NJ,USA,1999.DOI:10.1109/CEC.1999.785511.
[13] 纪震,廖惠连,吴青华.粒子群算法及应用[M].北京:科学出版社,2009.
[14] 姜长弘,张永恒,王盛慧.基于自适应粒子群算法的篦冷机电液位置饲服系统PID参数优化[J].液压与气动,2016(7):44-49.DOI:10.11832/j.issn.1000-4858.2016.07.009. JIANG C H,ZHANG Y H,WANG S H.PID parameter optimization of the gate cooling electro-hydraulic position servo system based on adaptive particle swarm optimization algorithm[J].Chinese Hydraulics & Pneumatics,2016(7):44-49.DOI:10.11832/j.issn.1000-4858.2016.07.009.
[15] 赵泽辉.高速公路交通量预测及山区高速公路后评价研究[D].大连:大连理工大学,2015. ZHAO Z H.Highway traffic forecasting and post-evaluation research of mountain highway[D].Dalian:Dalian University of Technology,2015.

基于ADPSO算法优化LSSVM的高速公路交通量预测方法

A forecasting method of highway traffic flow using LSSVM optimized by ADPSO algorithm

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 1

编辑推荐

Metrics

本文评价