A Combined Predicting Model for Expressway Travel Time Based on EMD-GRU

PENG Yong; ZHOU Xin; SONG Qiankun; XIANG Zhonghua

doi:10.21656/1000-0887.410165

Volume 42 Issue 4

Apr. 2021

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Article Navigation > Applied Mathematics and Mechanics > 2021 > 42(4): 405-412

PENG Yong, ZHOU Xin, SONG Qiankun, XIANG Zhonghua. A Combined Predicting Model for Expressway Travel Time Based on EMD-GRU[J]. Applied Mathematics and Mechanics, 2021, 42(4): 405-412. doi: 10.21656/1000-0887.410165

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A Combined Predicting Model for Expressway Travel Time Based on EMD-GRU

doi: 10.21656/1000-0887.410165

¹College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, P.R.China
²College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, P.R.China

Received Date: 2020-06-08
Rev Recd Date: 2020-06-15
Publish Date: 2021-04-01

Abstract

Abstract

In view of the variety of influential factors on the expressway travel time and the significance of nonlinear and non-stationary characteristics of the travel time series, a combined expressway travel time predicting model was designed based on the empirical mode decomposition and the GRU neural network. First, the time information of vehicles entering and exiting the expressway in toll data was used to obtain the travel time series of the road segments; then, the empirical mode decomposition algorithm was applied to decompose the complex travel time series into a number of relatively stable and different-time-scale eigen modal function components as well as residual components; then, the GRU neural network was used to predict and integrate the intrinsic modal function components and residual components. The example analysis shows that, the empirical mode decomposition can effectively improve the prediction accuracy of the LSTM and the GRU neural networks; under the same parameter settings, the prediction accuracy of the GRU neural network is better than that of the LSTM neural network.
- intelligent transportation,
- combined prediction,
- travel time,
- empirical mode decomposition,
- GRU neural network

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References(17)

References

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