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CLC number: TP393; U491.13

On-line Access: 2017-02-10

Received: 2015-11-03

Revision Accepted: 2016-02-26

Crosschecked: 2016-12-23

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Dong-wei Xu


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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.2 P.287-302


Real-time road traffic state prediction based on ARIMA and Kalman filter

Author(s):  Dong-wei Xu, Yong-dong Wang, Li-min Jia, Yong Qin, Hong-hui Dong

Affiliation(s):  College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; more

Corresponding email(s):   dongweixu@zjut.edu.cn

Key Words:  Autoregressive integrated moving average (ARIMA) model, Kalman filter, Road traffic state, Real-time, Prediction

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Dong-wei Xu, Yong-dong Wang, Li-min Jia, Yong Qin, Hong-hui Dong. Real-time road traffic state prediction based on ARIMA and Kalman filter[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(2): 287-302.

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publisher="Zhejiang University Press & Springer",

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T1 - Real-time road traffic state prediction based on ARIMA and Kalman filter
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DOI - 10.1631/FITEE.1500381

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.

This article describes how the author have implemented an ARIMA state space representation and used Kalman filtering for traffic condition predictions. This is an interesting idea.




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