基于ARIMA和Kalman滤波的道路交通状态实时预测

徐东伟; 王永东; 贾利民; 秦勇; 董宏辉

doi:10.1631/FITEE.1500381

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基于ARIMA和Kalman滤波的道路交通状态实时预测

常规文章 | Updated：2022-05-19

- 基于ARIMA和Kalman滤波的道路交通状态实时预测
  Enhanced Publication
- Real-time road traffic state prediction based on ARIMA and Kalman filter
- 信息与电子工程前沿（英文） 2017年18卷第2期页码：287-302
- Affiliations：
  
  1College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
  2United Key Laboratory of Embedded System of Zhejiang Province, Hangzhou 310023, China
  3State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University 100044, China
- Author bio：
  
  E-mail:dongweixu@zjut.edu.cn
- Funds：
  
  Project supported by the National Science & Technology Pillar Program (No. 2014BAG01B02)
- DOI：10.1631/FITEE.1500381
  中图分类号： TP393; U491.13
- 纸质出版日期：2017-02，
  
  收稿日期：2015-11-03，
  
  录用日期：2016-2-26
- Accepted：
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徐东伟, 王永东, 贾利民, 等. 基于ARIMA和Kalman滤波的道路交通状态实时预测[J]. 信息与电子工程前沿（英文）, 2017,18(2):287-302.

DONG-WEI XU, YONG-DONG WANG, LI-MIN JIA, et al. Real-time road traffic state prediction based on ARIMA and Kalman filter. [J]. Frontiers of information technology & electronic engineering, 2017, 18(2): 287-302.
徐东伟, 王永东, 贾利民, 等. 基于ARIMA和Kalman滤波的道路交通状态实时预测[J]. 信息与电子工程前沿（英文）, 2017,18(2):287-302. DOI： 10.1631/FITEE.1500381.

DONG-WEI XU, YONG-DONG WANG, LI-MIN JIA, et al. Real-time road traffic state prediction based on ARIMA and Kalman filter. [J]. Frontiers of information technology & electronic engineering, 2017, 18(2): 287-302. DOI： 10.1631/FITEE.1500381.

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。

Abstract

The realization of road traffic prediction not only provides real-time and effective information for travelers

but also helps them select the optimal route to reduce travel time. Road traffic prediction offers traffic guidance for travelers and relieves traffic jams. In this paper

a real-time road traffic state prediction based on autoregressive integrated moving average (ARIMA) and the Kalman filter is proposed. First

an ARIMA model of road traffic data in a time series is built on the basis of historical road traffic data. Second

this ARIMA model is combined with the Kalman filter to construct a road traffic state prediction algorithm

which can acquire the state

measurement

and updating equations of the Kalman filter. Third

the optimal parameters of the algorithm are discussed on the basis of historical road traffic data. Finally

four road segments in Beijing are adopted for case studies. Experimental results show that the real-time road traffic state prediction based on ARIMA and the Kalman filter is feasible and can achieve high accuracy.

关键词

ARIMA模型Kalman滤波建模训练预测

Keywords

Autoregressive integrated moving average (ARIMA) modelKalman filterRoad traffic stateReal-timePrediction

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