Traffic Flow Combined Prediction Model Based on Complementary Ensemble Empirical Mode Decomposition and Bidirectional Gated Recurrent Unit Optimized by Improved Sparrow Search Algorithm

YIN Lisheng; LIU Pan; SUN Shuangchen; WU Yangyang; SHI Cheng; HE Yigang

doi:10.11999/JEIT221172

Volume 45 Issue 12

Dec. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(12): 4499-4508

YIN Lisheng, LIU Pan, SUN Shuangchen, WU Yangyang, SHI Cheng, HE Yigang. Traffic Flow Combined Prediction Model Based on Complementary Ensemble Empirical Mode Decomposition and Bidirectional Gated Recurrent Unit Optimized by Improved Sparrow Search Algorithm[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4499-4508. doi: 10.11999/JEIT221172

Citation:

YIN Lisheng, LIU Pan, SUN Shuangchen, WU Yangyang, SHI Cheng, HE Yigang. Traffic Flow Combined Prediction Model Based on Complementary Ensemble Empirical Mode Decomposition and Bidirectional Gated Recurrent Unit Optimized by Improved Sparrow Search Algorithm[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4499-4508. doi: 10.11999/JEIT221172

Citation:

YIN Lisheng, LIU Pan, SUN Shuangchen, WU Yangyang, SHI Cheng, HE Yigang. Traffic Flow Combined Prediction Model Based on Complementary Ensemble Empirical Mode Decomposition and Bidirectional Gated Recurrent Unit Optimized by Improved Sparrow Search Algorithm[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4499-4508. doi: 10.11999/JEIT221172

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Traffic Flow Combined Prediction Model Based on Complementary Ensemble Empirical Mode Decomposition and Bidirectional Gated Recurrent Unit Optimized by Improved Sparrow Search Algorithm

doi: 10.11999/JEIT221172

1.
Hefei University of Technology, School of Electrical Engineering and Automation, Hefei 230009, China
2.
Wuhan University, School of Electrical Engineering, Wuhan 430072, China

Funds: The National Natural Science Foundation of China (62073114, 6207022417), The Natural Science Foundation of Anhui Province (JZ2021AKZR0344)

Received Date: 2022-09-08
Rev Recd Date: 2022-12-05

Available Online: 2022-12-08

Publish Date: 2023-12-26

Abstract

Abstract

In order to improve the accuracy and convergence speed of prediction, a combined prediction model, based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Bidirectional Gated Recurrent Unit (BiGRU) optimized by Improved Sparrow Search Algorithm (ISSA), is proposed to deal with the nonlinear, non-stationary and temporal correlation of short-term traffic flow prediction. Firstly, considering the end-point flying wing problem, the traffic flow sequence is decomposed into Intrinsic Mode Function (IMF) components that reflect the trend, periodicity and randomness of road network traffic by improved CEEMD algorithm, which extracts effectively the prior features; Then, the BiGRU network is used to mine the temporal correlation in traffic flow sequence. To fear the local optimum, and improve the global search and local mining ability of Sparrow Search Algorithm (SSA), ISSA is used instead of gradient descent method to iterate the BiGRU network weights. The ablation experiment results show that each component in the combined prediction model plays a positive role in improving the prediction accuracy. The prediction performance under different traffic flow sets is better than the compared algorithm, showing accurate and fast prediction performance with good generalization ability.

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References(19)

References

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