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

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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.

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Authors and Affiliations

  1. College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China

    Dong-wei Xu & Yong-dong Wang

  2. United Key Laboratory of Embedded System of Zhejiang Province, Hangzhou, 310023, China

    Dong-wei Xu & Yong-dong Wang

  3. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, 100044, China

    Li-min Jia, Yong Qin & Hong-hui Dong

Authors
  1. Dong-wei Xu
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  2. Yong-dong Wang
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  3. Li-min Jia
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  4. Yong Qin
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  5. Hong-hui Dong
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Corresponding author

Correspondence to Dong-wei Xu.

Additional information

Project supported by the National Science & Technology Pillar Program (No. 2014BAG01B02)

ORCID: Dong-wei XU, http://orcid.org/0000-0003-2693-922X

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Xu, Dw., Wang, Yd., Jia, Lm. et al. Real-time road traffic state prediction based on ARIMA and Kalman filter. Frontiers Inf Technol Electronic Eng 18, 287–302 (2017). https://doi.org/10.1631/FITEE.1500381

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  • DOI: https://doi.org/10.1631/FITEE.1500381

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