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
Clicked: 7103
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.
@article{title="Real-time road traffic state prediction based on ARIMA and Kalman filter",
author="Dong-wei Xu, Yong-dong Wang, Li-min Jia, Yong Qin, Hong-hui Dong",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="2",
pages="287-302",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500381"
}
%0 Journal Article
%T Real-time road traffic state prediction based on ARIMA and Kalman filter
%A Dong-wei Xu
%A Yong-dong Wang
%A Li-min Jia
%A Yong Qin
%A Hong-hui Dong
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 2
%P 287-302
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500381
TY - JOUR
T1 - Real-time road traffic state prediction based on ARIMA and Kalman filter
A1 - Dong-wei Xu
A1 - Yong-dong Wang
A1 - Li-min Jia
A1 - Yong Qin
A1 - Hong-hui Dong
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 2
SP - 287
EP - 302
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1500381
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.
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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