[1]沈国江,朱 芸,钱晓杰,等.短时交通流组合模型预测[J].南京理工大学学报(自然科学版),2014,38(02):246-251.
 Shen Guojiang,Zhu Yun,Qian Xiaojie,et al.Short-term traffic flow forecasting based on hybrid model[J].Journal of Nanjing University of Science and Technology,2014,38(02):246-251.
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短时交通流组合模型预测
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
38卷
期数:
2014年02期
页码:
246-251
栏目:
出版日期:
2014-04-30

文章信息/Info

Title:
Short-term traffic flow forecasting based on hybrid model
作者:
沈国江1朱 芸2钱晓杰2胡 越2
1.浙江工业大学 计算机科学与技术学院,浙江 杭州 310023; 2.浙江大学 工业控制国家重点实验室,浙江 杭州 310027
Author(s):
Shen Guojiang1Zhu Yun2Qian Xiaojie2Hu Yue2
1.College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China; 2.State Key Laboratory of Industrial Control Technology,Zhejiang University,Hangzhou 310027,China
关键词:
间断流 短时交通流预测 卡尔曼滤波模型 径向基函数神经网络 惯性因子
Keywords:
interrupted flow short-term traffic flow forecasting Kalman filter model radical basis function neural network inertia factors
分类号:
TP18
摘要:
针对城市道路流量的非线性和不确定性特点,为避免单一模型预测准确率不高的缺陷,该文提出了一种短时交通流组合模型。该模型包含卡尔曼滤波模型和径向基函数神经网络模型2个子模型,较好地解决了神经网络不能反映大流量下的稳态性问题,以及卡尔曼滤波在流量不稳定时预测准确率不高的问题。在组合模型中引入惯性因子,确保了模型的稳定性。仿真结果表明该方法是可行有效的。
Abstract:
In view of that the traffic flow of the urban road is a nonlinear and uncertaint interrupted flow,a hybrid model for the short-term traffic flow is put forward to overcome the shortage of the lower forecasting accuracy of the single model.This model consists of two sub-models,the Kalman filter model and the radical basis function neural network model,so the steady-state problem of the neural network model in the huge traffic flow and the low accuracy problem of the Kalman filter model in the unsteady traffic flow can be all solved.An inertia factor is introduced in the process of combining to ensure the stability of the hybrid model.The simulation result shows that the hybrid model is feasible and effective.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2013-06-06 修回日期:2013-11-28
基金项目:国家自然科学基金(61174174); 中央高校基本科研业务专项资金(2013XZZX008-1); 浙江省交通运输厅科研计划项目(2014T08)
作者简介:沈国江(1975-),男,博士,副教授,主要研究方向:智能交通,E-mail:gjshen@iipc.zju.edu.cn。
引文格式:沈国江,朱芸,钱晓杰,等.短时交通流组合模型预测[J].南京理工大学学报,2014,38(2):246-251.
投稿网址:http://njlgdxxb.paperonce.org
更新日期/Last Update: 2014-04-30