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Volume 6 Issue 6
Nov.  2019

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2019  >  6(6): 1344-1351
Runmei Li, Yinfeng Huang and Jian Wang, "Long-term Traffic Volume Prediction Based on K-means Gaussian Interval Type-2 Fuzzy Sets," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1344-1351, Nov. 2019. doi: 10.1109/JAS.2019.1911723
Citation: Runmei Li, Yinfeng Huang and Jian Wang, "Long-term Traffic Volume Prediction Based on K-means Gaussian Interval Type-2 Fuzzy Sets," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1344-1351, Nov. 2019. doi: 10.1109/JAS.2019.1911723
shu

Long-term Traffic Volume Prediction Based on K-means Gaussian Interval Type-2 Fuzzy Sets

doi: 10.1109/JAS.2019.1911723
Funds:  This work was supported by the National Key Research and Development Program of China (2018YFB1201500)
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    Abstract
  • This paper uses Gaussian interval type-2 fuzzy set theory on historical traffic volume data processing to obtain a 24- hour prediction of traffic volume with high precision. A K-means clustering method is used in this paper to get 5 minutes traffic volume variation as input data for the Gaussian interval type-2 fuzzy sets which can reflect the distribution of historical traffic volume in one statistical period. Moreover, the cluster with the largest collection of data obtained by K-means clustering method is calculated to get the key parameters of type-2 fuzzy sets, mean and standard deviation of the Gaussian membership function. Using the range of data as the input of Gaussian interval type-2 fuzzy sets leads to the range of traffic volume forecasting output with the ability of describing the possible range of the traffic volume as well as the traffic volume prediction data with high accuracy. The simulation results show that the average relative error is reduced to 8% based on the combined K-means Gaussian interval type-2 fuzzy sets forecasting method. The fluctuation range in terms of an upper and a lower forecasting traffic volume completely envelopes the actual traffic volume and reproduces the fluctuation range of traffic flow.

     

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    Highlights

    • We apply type-2 fuzzy sets theory in long-term traffic volume predictions. Simulation results indicate the method oposed in the paper gives full play to the ability of type-2 fuzzy set theory to deal with uncertainty data.
    • We propose a data-driven approach to construct the interval type-2 fuzzy sets which makes the construction of the sets more convincing. Meanwhile we transform traffic flow data into traffic states as to obtain the characteristics of traffic flow innovatively, which are ultimately used to construct the embedded type-1 fuzzy sets.
    • Finally we present a traffic volume forecasting model which can estimate the probability distribution of traffic volume at the same time horizon along with the highly accurate traffic volume prediction results.

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