全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于SVM与自适应时空数据融合的短时交通流量预测模型

Keywords: 短时交通流预测,支持向量机,自适应,数据融合,相关分析

Full-Text   Cite this paper   Add to My Lib

Abstract:

针对短时交通流变化周期性与随机性特点,选取时间和空间序列流量观测值作为支持向量机训练样本进行训练,使用空间序列预测值对交通流时间序列预测结果进行修正,并通过对历史时间空间序列预测结果的分析,动态调整其对未来预测的影响,建立基于SVM与自适应时空数据融合的短时交通流量预测模型.最后,将提出的预测模型与支持向量机时间序列预测模型、指数平滑法、多元回归法预测结果进行对比,结果表明:自适应时空数据融合预测模型可将预测平均相对误差控制在4%,明显高于其他模型预测精度.

References

[1]  CHUNHSIN W, CHIACHEN W. Travel time prediction with support vector regression[J]. IEEE Transaction on Intelligent Transportation Systems, 2004, 5(12): 276- 281.
[2]  徐启华, 杨瑞. 支持向量机在交通流量实时预测中的应用[J]. 公路交通科技, 2005, 22(12): 131-134.
[3]  XU Qi-hua, YANG Rui. Traffic flow prediction using support vector machine based method [ J]. Journal of Highway and Transportation Research and Development,2005, 22(12): 131-134. (in Chinese)
[4]  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.
[5]  YANG Zhao-sheng, WANG Yuan, GUAN Qing. Short-
[6]  term traffic flow prediction method based on SVM[J]. Journal of Jilin University: Engineering and Technology Edition, 2006, 36(6): 881-884. (in Chinese)
[7]  傅贵, 韩国强, 逯峰, 等. 基于支持向量机回归的短时交通流预测模型[J]. 华南理工大学学报: 自然科学版, 2013, 41(9): 71-76.
[8]  FU Gui, HAN Guo-qiang, LU Feng, et al. Short-term
[9]  traffic flow forecasting model based on support vector machine regression[J]. Journal of South China University of Technology: Natural Science Edition, 2013, 41(9):
[10]  71-76. (in Chinese)
[11]  李巧茹, 陈亮, 张铮, 等. 并行式时空二维融合路段交通量预测[J]. 河北工业大学学报, 2008, 37(3):112-116.
[12]  LI Qiao-ru, CHEN Liang, ZHANG Zheng, et al. Parallel
[13]  spatio-temporal data fusion on traffic flow prediction of road section [ J ]. Journal of Hebei University of Technology, 2008, 37(3): 112-116. (in Chinese)
[14]  董西国. 支釽_k6瞋も持向量机在数据挖掘中的应用
[15]  HUSSEIN D. An objected-oriented neural network approach to short-term traffic forecasting[J]. Special Issue of the European Journal of Operation Research, 2001, 131(2): 235-261.
[16]  范鲁明. 基于非参数回归的短时交通流量预测[D]. 天津: 天津大学管理学院, 2007: 27-30.
[17]  FAN Lu-ming. Short-term traffic flow forecasting based on nonparametric regression [ D ]. Tianjin: School of Management, Tianjin University, 2007: 27-30. (in Chinese)
[18]  KUN H, SENFA C, ZHOU Zhen-guo, et al. Research on a non-linear chaotic prediction model for urban traffic flow [J]. Journal of Southeast University, 2003, 19(4): 410-413.
[19]  尚宁, 覃明贵, 王亚琴, 等. 基于BP 神经网络的路口短时交通流量预测方法[J]. 计算机应用与软件,2006, 23(2): 32-33.
[20]  SHANG Ning, QIN Ming-gui, WANG Ya-qin, et al. A BP neural network method for short-term traffic flow forecasting on crossroads[J]. Computer Applications and Software, 2006, 23(2): 32-33. (in Chinese)
[21]  樊娜, 赵祥模, 戴明, 等. 短时交通流预测模型[J].交通运输工程学报, 2012, 12(4): 114-119.
[22]  FAN Na, ZHAO Xiang-mo, DAI Ming, et al. Short-term
[23]  traffic flow prediction model[J]. Journal of Traffic and Transportation Engineering, 2012, 12(4): 114-119. (in Chinese)
[24]  高尚, 房靖. 公路交通运量的支持向量机组合预测[J]. 交通运输系统工程与信息, 2007, 7(4): 106-110.
[25]  GAO Shang, FANG Jing. Road traffic freight volume forecast based on support vector machine combining forecasting [ J ]. Journal of Transportation Systems Engineering and Information Technology, 2007, 7 (4):106-110. (in Chinese)
[26]  李颖宏, 刘乐敏, 王玉全. 基于组合预测模型的短时交通流预测[J]. 交通运输系统工程与信息, 2013, 13(2): 34-41.
[27]  LI Ying-hong, LIU Le-min, WANG Yu-quan. Short-term
[28]  traffic flow prediction based on combination of predictive models[J]. Journal of Transportation Systems Engineering and Information Technology, 2013, 13(2): 34-41. (in Chinese)
[29]  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, 27(2):73-76.
[30]  LI Bin, XI Tao, SHI Ming-hua, et al. Traffic flow
[31]  combined forecast model of support vector machine[J]. Journal of Tianjin Polytechnic University, 2008, 27(2):73-76. (in Chinese)
[32]  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, 31(3): 561-568.
[33]  SHEN Guo-jiang, WANG Xiao-hu, KONG Xiang-jie, et al. Short-term traffic volume intelligent hybrid forecasting model and its application[J]. Systems Engineering Theory & Practice, 2011, 31(3): 561-568. (in Chinese)

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133