一种结合SVM与卡尔曼滤波的短时交通流预测模型

计算机科学 ›› 2013, Vol. 40 ›› Issue (10): 248-251.

一种结合SVM与卡尔曼滤波的短时交通流预测模型

朱征宇,刘琳,崔明

重庆大学计算机学院重庆400044 软件工程重庆市重点实验室重庆400044;重庆大学计算机学院重庆400044 软件工程重庆市重点实验室重庆400044;重庆大学计算机学院重庆400044 软件工程重庆市重点实验室重庆400044

出版日期:2018-11-16 发布日期:2018-11-16
基金资助:
本文受科技部国家科技支撑计划重点项目(2011BAH25B041)资助

Short-term Traffic Flow Forecasting Model Combining SVM and Kalman Filtering

ZHU Zheng-yu,LIU Lin and CUI Ming

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 针对短时交通流量的预测问题,提出了一种结合卡尔曼滤波与支持向量机的预测模型。该模型采用预测误差平方和与相关系数极大化准则智能选取恰当的预测方式,综合利用了支持向量机的稳定性与卡尔曼滤波的实时性,发挥了两种模型各自的优势。实验结果表明,该模型误差指标均低于单项预测模型。特别地,该模型在高峰时段的预测性能最佳,平均相对误差保持在8%以内,是短时交通流预测的一种有效可行的方法。

关键词: 交通流,组合预测,支持向量机,卡尔曼滤波

Abstract: Aiming at the issue about short-term traffic flow forecasting,a prediction model combining with Kalman filtering and support vector machine was proposed．The model adopts appropriate forecast method intelligently in each prediction period by the standards of error sum of squares and vector cosine of the angle,utilizes the stability of SVM and the real-time nature of Kalman filter comprehensively,and takes respective advantages of the two models．Experiments show that the model’s error indicators are lower than single forecast model．In particular,the model in the peak hours,which average relative error is maintained at less than 8%,is a feasible and effective method of short-term traffic flow forecasting.

Key words: Traffic flow,Combining forecasting,Support vector machine,Kalman filter

朱征宇,刘琳,崔明. 一种结合SVM与卡尔曼滤波的短时交通流预测模型[J]. 计算机科学, 2013, 40(10): 248-251. https://doi.org/

ZHU Zheng-yu,LIU Lin and CUI Ming. Short-term Traffic Flow Forecasting Model Combining SVM and Kalman Filtering[J]. Computer Science, 2013, 40(10): 248-251. https://doi.org/

参考文献

[1] 邵春福,熊志华,姚智胜.道路网短时交通需求预测理论、方法及应用[M].北京:清华大学出版社,2011
[2] 许伦辉,傅惠.交通信息智能预测理论与方法[M]．北京:科学出版社,2009
[3] 宋驰,沈国江．短时交通流预测模型综述[J].自动化博览,2012(6):84-87
[4] 陈华友,盛昭瀚,刘春林,等.基于向量夹角余弦的组合预测模型的性质研究[J].管理科学学报,2006,9(2):1-8
[5] 孙李红,沈继红．基于相关系数的加权几何平均组合预测模型的性质[J].系统工程理论与实践,2009,9(9):84-91
[6] 李斌,郗涛,史明华,等.基于支持向量机的交通流组合预测模型[J].天津工业大学学报,2008,7(2):73-76
[7] 韩冬梅,牛文清,杨荣,等.线性与非线性最优组合预测方法的比较研究[J].情报科学,2007,5(11):1672-1678
[8] 张大斌,张景广,彭森,等.基因表达式编程在组合预测建模中的应用[J].系统工程理论与实践,2012,2(3):568-573
[9] Wu Chun-hsin,Wei Chia-chen,Chang Ming-hua,et al．Traveltime prediction with support vector regression[J]．IEEE Tran-saction on Intelligent Transportation systems,2004,5(12):276-281
[10] Theja P V V K,Vanajakshi L．Short Term Prediction of Traffic Parameters Using Support Vector Machines Technique [C]∥ Emerging Trends in Engineering and Technology(ICETET),20103rd International Conference on．Goa,India,2010:70-75
[11] 龚珊,尹相勇,朱爱华,等.基于浮动车的路段行程时间卡尔曼滤波预测算法[C]∥2008第四届中国智能交通年会论文集.2008:1-6
[12] Chang Ming-wei,Lin Chi-hjen．Leave-one-out Bounds for Support Vector Regression Model Selection[J]．Neural Computation,2005,7(5):1188-1222
[13] Kim K J．Financial Time Series Forecasting Using Support Vector Machines[J]．Neurocomputing,2003,5(3):307-319
[14] Kalman R．A new approach to linear filtering and predictionproblems[J].Journal of Basic Engineering,1960,2(01):35-46
[15] 陆如华,徐传玉,张玲,等.卡尔曼滤波的初值计算方法及其应用[J].应用气象学报,1999,2(3):63-67
[16] 杨兆升,王媛,管青,等.基于支持向量机方法的短时交通流量预测方法[J].吉林大学学报:工学版,2006,6(6):881-884
[17] 沈国江,王啸虎,孔祥杰,等.短时交通流量智能组合预测模型及应用[J].系统工程理论与实践,2011,1(3):561-568
[18] 张涛,陈先,谢美萍,等.基于K近邻非参数回归的短时交通流预测方法[J].系统工程理论与实践,2010,0(2):376-384

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