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A multivariate short-term traffic flow forecasting method based on wavelet analysis and seasonal time series

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Abstract

Short-term traffic flow forecasting is a key step to achieve the performance of intelligent transportation system (ITS). Timely and accurate traffic information prediction is also the prerequisite of realizing proactive traffic control and dynamic traffic assignment effectively. Based on the fact that univariate forecasting methods have limited forecasting abilities when the data is missing or erroneous and that single models make no full use of information underline data, a new hybrid method with multivariate for short-term traffic flow forecasting is proposed. This method combines statistical analysis method with computational intelligence techniques to mine the characteristic of traffic flow as well as forecast short-term traffic state. First, the wavelet de-noising is employed to remove the noise information. Then, time series analysis is used to analyze time-varying and periodic characteristic of traffic flow. Furthermore, the seasonal auto-regressive moving average with external input (SARIMAX) is established to fit traffic flow with occupancy as exogenous variables. Finally, wavelet forecast is adopted to forecast the values of occupancy which are used as exogenous input, and a WSARIMAX is constructed to forecast traffic flow. Using the relationship of flow and occupancy at the same road section and taking traffic flow and occupancy data from freeway I-694 EB in the Twin Cities as endogenous variables and exogenous variables respectively, this paper studies the forecasting performance of the proposed method. The study results are encouraging. Compared with SARIMA newly proposed in literature, WSARIMA and SARIMAX improved method with wavelet analysis and multivariate modeling method, the proposed method gets improvements of 12.95%, 12.62% and 10.41% in forecasting accuracy of one-step ahead respectively. For ten-steps ahead forecasting, it gets improvements of 18.87%, 17.05% and 2.57% in forecasting accuracy respectively.

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Acknowledgments

The authors are grateful to the anonymous reviewers for their comments, which will help to improve our paper.

Funding

This work was supported by National Natural Science Foundation of China [Grant No.61663021]; Scientific Research Project in Universities of Gansu [Grant No. 2015B-031]; Science and Technology Support Program of Gansu [Grant No.1304GKCA023].

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Authors and Affiliations

  1. College of Computer & Communication, Lanzhou University of Technology, Lanzhou, 730050, China

    Hong Zhang & Jie Cao

  2. College of Electrical & Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

    Hong Zhang, Xiaoming Wang & Yirong Guo

  3. College of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China

    Minan Tang

  4. College of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

    Minan Tang

Authors
  1. Hong Zhang
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  2. Xiaoming Wang
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  3. Jie Cao
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  4. Minan Tang
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  5. Yirong Guo
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Correspondence to Hong Zhang.

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Zhang, H., Wang, X., Cao, J. et al. A multivariate short-term traffic flow forecasting method based on wavelet analysis and seasonal time series. Appl Intell 48, 3827–3838 (2018). https://doi.org/10.1007/s10489-018-1181-7

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  • DOI: https://doi.org/10.1007/s10489-018-1181-7

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