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

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...

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Bibliographic Details
Published inFrontiers of information technology & electronic engineering Vol. 18; no. 2; pp. 287 - 302
Main Authors Xu, Dong-wei, Wang, Yong-dong, Jia, Li-min, Qin, Yong, Dong, Hong-hui
Format Journal Article
LanguageEnglish
Published Hangzhou Zhejiang University Press 01.02.2017
Springer Nature B.V
Subjects
Algorithms
Autoregressive models
Communications Engineering
Computer Hardware
Computer Science
Computer Systems Organization and Communication Networks
Electrical Engineering
Electronics and Microelectronics
Instrumentation
Kalman filters
Networks
Real time
Roads
Route selection
Traffic
Traffic congestion
Traffic information
Traffic jams
Traffic models
Travel time
TP393
Kalman filter
Real-time
Road traffic state
Prediction
Autoregressive integrated moving average (ARIMA) model
U491.13
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Summary: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.
Bibliography:Autoregressive integrated moving average （ARIMA） model; Kalman filter; Road traffic state; Real-time; Prediction
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.
33-1389/TP
ISSN:2095-9184
2095-9230
DOI:10.1631/FITEE.1500381