基于ARIMA和Kalman滤波的道路交通状态实时预测（英文）

被引：26

作者：

Dongwei XU ^{[1
,2
]}

Yongdong WANG ^{[1
,2
]}

Limin JIA ^{[3
]}

Yong QIN ^{[3
]}

Honghui DONG ^{[3
]}

机构：

[1] College of Information Engineering, Zhejiang University of Technology

[2] United Key Laboratory of Embedded System of Zhejiang Province

[3] State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong

来源：

FrontiersofInformationTechnology&ElectronicEngineering | 2017年 / 18卷 / 02期

关键词：

ARIMA模型; Kalman滤波; 建模; 训练; 预测;

D O I：

暂无

中图分类号：

U491.14 []; TN713 [滤波技术、滤波器];

学科分类号：

摘要：

道路交通流预测不仅可以为出行者提供实时有效的信息,而且可以帮助他们选择最佳路径,减少出行时间,实现道路交通路径诱导,缓解交通拥堵。本文提出了一种基于ARIMA模型和Kalman滤波算法的道路交通流预测方法。首先,基于道路交通历史数据建立时间序列的ARIMA模型。其次,结合ARIMA模型和Kalman滤波法构建道路交通预测算法,获取Kalman滤波的测量方程和更新方程。然后,基于历史道路交通数据进行算法的参数设定。最后,以北京的四条路段作为案例,对所提出的方法进行了分析。实验结果表明,基于ARIMA模型和Kalman滤波的实时道路交通状态预测方法是可行的,并且可以获得很高的精度。

引用

页码：287 / 303

页数：17

共 18 条

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