吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (03): 646-653.doi: 10.7964/jdxbgxb201303015

• 论文 • 上一篇    下一篇

基于小波和回声状态网络的交通流多步预测模型

杨飞1,2,3, 方滨兴1,2, 王春露1,2, 左兴权1,2, 李丽香1,2, 平源1,2   

  1. 1. 北京邮电大学 计算机学院,北京 100876;
    2. 北京邮电大学 可信分布式计算与服务教育部重点实验室,北京 100876;
    3. 南京熊猫电子股份有限公司,南京 210002
  • 收稿日期:2012-01-17 出版日期:2013-05-01 发布日期:2013-05-01
  • 作者简介:杨飞(1979-),男,工程师,博士研究生.研究方向:机器学习与智能交通,信号处理. E-mail:yangfei800103@163.com
  • 基金资助:

    国家自然科学基金项目(60973009,61170269);国家科技支撑计划项目(2009BAG13A01);国家教育部新世纪优秀人才支持计划项目(NCET-10-0239);国家教育部霍英东教育基金会项目(121062);中央高校基本科研业务费项目(2009RC0208).

Multi-step traffic flow prediction model based on wavelet and echo state network

YANG Fei1,2,3, FANG Bin-xing1,2, WANG Chun-lu1,2, ZUO Xing-quan1,2, LI Li-xiang1,2, PING Yuan1,2   

  1. 1. School of Computer Science, Beijing University of Post and Telecommunications, Beijing 100876, China;
    2. Key Laboratory of Trustworthy Distributed Computing and Service of the Ministry of Education of China, Beijing University of Post and Telecommunications, Beijing 100876, China;
    3. Nanjing Panda Electronics Company Limited, Nanjing 210002, China
  • Received:2012-01-17 Online:2013-05-01 Published:2013-05-01

摘要: 针对交通流的含噪混沌特征,提出了一种基于小波回声状态网络的交通流多步预测模型.该模型利用小波多尺度分解方法,屏蔽了噪声成分对交通流动力学特性的干扰,同时提取了占有交通流绝大部分能量的混沌低频成分.在采用多路分量并行预测的方式下,充分发挥了回声状态网络对混沌低频分量的强大多步预测能力,从而保障了交通流多步预测的精度.对北京市西直门桥的实测交通流的预测结果表明:该模型的多步预测精度比传统的回声状态网络模型有了较大幅度的提升,在保证预测精度的前提下,最大可预测的步长也相应的增加.

关键词: 交通运输系统工程, 交通流预测, 回声状态网络, 混沌吸引子, 相空间重构

Abstract: In light of the noisy chaotic characteristics of traffic flow, a new multi-step traffic flow prediction model based on wavelet and echo state network was proposed. Utilizing multi-scale decomposition method of wavelet, the proposed model restricts the interference of noisy components to the dynamic behavior of the traffic flow; meanwhile it extracts the chaotic low-frequency component, which possesses most of the energy of traffic flow. In predicting the multi components concurrently, the strong prediction capacity of echo state network for chaotic low-frequency component was utilized effectively to ensure the accuracy of multi-step traffic flow prediction. The results of prediction of the real traffic flow in Xizhimen Bridge of Beijing show that the prediction accuracy is significantly improved by the proposed multi-step model in comparison with the traditional echo state network model. Under the condition of high prediction accuracy, the maximum predictable step is also increased by the proposed model.

Key words: engineering of communications and transportation system, traffic flow prediction, echo state network, chaotic attractor, phase space reconstruction

中图分类号: 

  • U491
[1] Qu L, Li L, Zhang Y, et al. PPCA-based missing data imputation for traffic flow volume: a systematical approach[J]. IEEE Transactions on Intelligent Transportation Systems, 2009, 10(3):512-521.

[2] 杨兆升, 王媛, 管青. 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, 2006,36(6):881-884. Yang Zhao-sheng, Wang Yuan, Guan Qing. Short-term traffic flow prediction method based on SVM[J]. Journal of Jilin University (Engineering and Technology Edition), 2006, 36(6): 881-884.

[3] 李松, 刘力军, 解永乐. 遗传算法优化BP神经网络的短时交通流混沌预测[J]. 控制与决策,2011, 26(10):1581-1585. Li Song, Liu Li-jun, Xie Yong-le. Chaotic prediction for short-term traffic flow of optimized BP neural network based on genetic algorithm[J].Control and Decision, 2011, 26(10):1581-1585.

[4] Tan M C, Wong S C, Xu J M, et al. An aggregation approach to short-term traffic flow prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2009, 10(1): 60-69.

[5] 董宏辉, 孙晓亮, 贾利民, 等. 多模态的交通流量预测模型[J]. 吉林大学学报:工学版,2011,41(3):645-649. Dong Hong-hui, Sun Xiao-liang, Jia Li-min, et al. Multimode traffic volume prediction model[J]. Journal of Jilin University (Engineering and Technology Edition), 2011, 41(3): 645-649.

[6] 殷礼胜, 鲁照权, 董学平.交通流量小波神经网络多步预测研究[J]. 自动化仪表,2011,32(8):7-10. Yin Li-sheng, Lu Zhao-quan, Dong Xue-ping. Research on the multi-step prediction of wavelet neural network for traffic flow[J].Process Automation Instrumentation, 2011, 32(8): 7-10.

[7] 姜桂艳, 常安德, 牛世峰, 等. 基于BP神经网络的交通数据序列动态可预测性分析方法[J].北京工业大学学报,2011, 37(7):1019-1026. Jiang Gui-yan, Chang An-de, Niu Shi-feng, et al. Dynamic predictability analysis for traffic data serials based on BP neural network[J]. Journal of Beijing University of Technology, 2011, 37(7): 1019-1026.

[8] Smith B L, Demetsky M J. Traffic flow forecasting: comparison of modeling approaches[J]. Journal of Transportation Engineering, 1997, 123(4): 261-266.

[9] Xu M, Gao Z Y. Chaos in a dynamic model of urban transportation network flow based on user equilibrium states[J]. Chaos, Solitons & Fractals,2009, 39(2): 586-598.

[10] 王进, 史其信. 短时交通流预测模型综述[J]. ITS通讯,2005,1(1):10-13. Wang Jin, Shi Qi-xin. A review of the short-term traffic flow prediction methods[J]. Intelligent Transportation Systems, 2005,1(1):10-13.

[11] Jaeger H, Haas H. Harnessing nonlinearity: prediction of chaotic time series with neural networks[J]. Science, 2004, 304(5667):78-80.

[12] Jaeger H, Lukosevicius M, Popovici D, et al. Optimization and applications of echo state networks with leaky-integrator neurons[J]. Neural Networks, 2007, 20: 335-352.

[13] 彭宇, 王建民, 彭喜元. 基于回声状态网络的时间序列预测方法研究[J].电子学报,2010,38(增刊1):148-154. Peng Yu, Wang Jian-min, Peng Xi-yuan. Researches on time series prediction with echo state networks[J]. Acta Electronica Sinica, 2010, 38(Sup.1):148-154.

[14] Lgawa K, Ohashi H. A negative selection algorithm for classification and reduction of the noise effect[J]. Applied Soft Computing, 2009, 9(1): 431-438.
[1] 陈永恒,刘芳宏,曹宁博. 信控交叉口行人与提前右转机动车冲突影响因素[J]. 吉林大学学报(工学版), 2018, 48(6): 1669-1676.
[2] 常山,宋瑞,何世伟,黎浩东,殷玮川. 共享单车故障车辆回收模型[J]. 吉林大学学报(工学版), 2018, 48(6): 1677-1684.
[3] 曲大义,杨晶茹,邴其春,王五林,周警春. 基于干线车流排队特性的相位差优化模型[J]. 吉林大学学报(工学版), 2018, 48(6): 1685-1693.
[4] 宗芳, 齐厚成, 唐明, 吕建宇, 于萍. 基于GPS数据的日出行模式-出行目的识别[J]. 吉林大学学报(工学版), 2018, 48(5): 1374-1379.
[5] 刘翔宇, 杨庆芳, 隗海林. 基于随机游走算法的交通诱导小区划分方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1380-1386.
[6] 钟伟, 隽志才, 孙宝凤. 不完全网络的城乡公交一体化枢纽层级选址模型[J]. 吉林大学学报(工学版), 2018, 48(5): 1387-1397.
[7] 刘兆惠, 王超, 吕文红, 管欣. 基于非线性动力学分析的车辆运行状态参数数据特征辨识[J]. 吉林大学学报(工学版), 2018, 48(5): 1405-1410.
[8] 宗芳, 路峰瑞, 唐明, 吕建宇, 吴挺. 习惯和路况对小汽车出行路径选择的影响[J]. 吉林大学学报(工学版), 2018, 48(4): 1023-1028.
[9] 栾鑫, 邓卫, 程琳, 陈新元. 特大城市居民出行方式选择行为的混合Logit模型[J]. 吉林大学学报(工学版), 2018, 48(4): 1029-1036.
[10] 陈永恒, 刘鑫山, 熊帅, 汪昆维, 谌垚, 杨少辉. 冰雪条件下快速路汇流区可变限速控制[J]. 吉林大学学报(工学版), 2018, 48(3): 677-687.
[11] 王占中, 卢月, 刘晓峰, 赵利英. 基于改进和声搜索算法的越库车辆排序[J]. 吉林大学学报(工学版), 2018, 48(3): 688-693.
[12] 李志慧, 胡永利, 赵永华, 马佳磊, 李海涛, 钟涛, 杨少辉. 基于车载的运动行人区域估计方法[J]. 吉林大学学报(工学版), 2018, 48(3): 694-703.
[13] 陈松, 李显生, 任园园. 公交车钩形转弯交叉口自适应信号控制方法[J]. 吉林大学学报(工学版), 2018, 48(2): 423-429.
[14] 苏书杰, 何露. 步行交通规划交叉路口行人瞬时动态拥塞疏散模型[J]. 吉林大学学报(工学版), 2018, 48(2): 440-447.
[15] 孟品超, 李学源, 贾洪飞, 李延忠. 基于滑动平均法的轨道交通短时客流实时预测[J]. 吉林大学学报(工学版), 2018, 48(2): 448-453.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!