GRU-LSTM model based on the SSA for short-term traffic flow prediction
The transportation department relies on accurate traffic forecasting for effective decision-making. However, determining relevant parameters for existing traffic flow prediction models poses challenges. To address this issue, this study proposes a hybrid model, sparrow search algorithm-gated recurre...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2025-03-01
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| Series: | Journal of Intelligent and Connected Vehicles |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/JICV.2024.9210051 |
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| _version_ | 1849326142295638016 |
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| author | Changxi Ma Xiaoyu Huang Yongpeng Zhao Tao Wang Bo Du |
| author_facet | Changxi Ma Xiaoyu Huang Yongpeng Zhao Tao Wang Bo Du |
| author_sort | Changxi Ma |
| collection | DOAJ |
| description | The transportation department relies on accurate traffic forecasting for effective decision-making. However, determining relevant parameters for existing traffic flow prediction models poses challenges. To address this issue, this study proposes a hybrid model, sparrow search algorithm-gated recurrent unit-long short-term memory (SSA-GRU-LSTM), which leverages the SSA to optimize the GRUs and LSTM networks. The SSA is employed to identify the optimal parameters for the GRU-LSTM model, mitigating their impact on prediction accuracy. This model integrates the predictive efficiency of the GRU, LSTM’s capability in temporal data analysis, and the fast convergence and global search attributes of the SSA. Comprehensive experiments are conducted to validate the proposed method via traffic flow datasets, and the results are compared with those of baseline models. The numerical results demonstrate the superior performance of the SSA-GRU-LSTM model. Compared with the baselines, the proposed model results in reductions in the root mean square error (RMSE) of 4.632%–45.206%, the mean absolute error (MAE) of 2.608%–53.327%, the mean absolute percentage error (MAPE) of 1.324%–13.723%, and an increase in R2 of 0.5%–17.5%. Consequently, the SSA-GRU-LSTM model has high prediction accuracy and measurement stability. |
| format | Article |
| id | doaj-art-63397c0010ba4873b193d98b04258db4 |
| institution | Kabale University |
| issn | 2399-9802 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Journal of Intelligent and Connected Vehicles |
| spelling | doaj-art-63397c0010ba4873b193d98b04258db42025-08-20T03:48:14ZengTsinghua University PressJournal of Intelligent and Connected Vehicles2399-98022025-03-0181921005110.26599/JICV.2024.9210051GRU-LSTM model based on the SSA for short-term traffic flow predictionChangxi Ma0Xiaoyu Huang1Yongpeng Zhao2Tao Wang3Bo Du4School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, ChinaSchool of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, ChinaGansu Highway Traffic Construction Group Co., Ltd., Lanzhou 730000, ChinaSchool of Architecture and Transportation Engineering, Guilin University of Electronic Technology, Guilin 541004, ChinaDepartment of Business Strategy and Innovation, Griffith University, Brisbane QLD 4111, AustraliaThe transportation department relies on accurate traffic forecasting for effective decision-making. However, determining relevant parameters for existing traffic flow prediction models poses challenges. To address this issue, this study proposes a hybrid model, sparrow search algorithm-gated recurrent unit-long short-term memory (SSA-GRU-LSTM), which leverages the SSA to optimize the GRUs and LSTM networks. The SSA is employed to identify the optimal parameters for the GRU-LSTM model, mitigating their impact on prediction accuracy. This model integrates the predictive efficiency of the GRU, LSTM’s capability in temporal data analysis, and the fast convergence and global search attributes of the SSA. Comprehensive experiments are conducted to validate the proposed method via traffic flow datasets, and the results are compared with those of baseline models. The numerical results demonstrate the superior performance of the SSA-GRU-LSTM model. Compared with the baselines, the proposed model results in reductions in the root mean square error (RMSE) of 4.632%–45.206%, the mean absolute error (MAE) of 2.608%–53.327%, the mean absolute percentage error (MAPE) of 1.324%–13.723%, and an increase in R2 of 0.5%–17.5%. Consequently, the SSA-GRU-LSTM model has high prediction accuracy and measurement stability.https://www.sciopen.com/article/10.26599/JICV.2024.9210051traffic flow predictionhybrid modelsparrow search algorithm (ssa)long short-term memory (lstm) networkgated recurrent unit (gru) |
| spellingShingle | Changxi Ma Xiaoyu Huang Yongpeng Zhao Tao Wang Bo Du GRU-LSTM model based on the SSA for short-term traffic flow prediction Journal of Intelligent and Connected Vehicles traffic flow prediction hybrid model sparrow search algorithm (ssa) long short-term memory (lstm) network gated recurrent unit (gru) |
| title | GRU-LSTM model based on the SSA for short-term traffic flow prediction |
| title_full | GRU-LSTM model based on the SSA for short-term traffic flow prediction |
| title_fullStr | GRU-LSTM model based on the SSA for short-term traffic flow prediction |
| title_full_unstemmed | GRU-LSTM model based on the SSA for short-term traffic flow prediction |
| title_short | GRU-LSTM model based on the SSA for short-term traffic flow prediction |
| title_sort | gru lstm model based on the ssa for short term traffic flow prediction |
| topic | traffic flow prediction hybrid model sparrow search algorithm (ssa) long short-term memory (lstm) network gated recurrent unit (gru) |
| url | https://www.sciopen.com/article/10.26599/JICV.2024.9210051 |
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