A Hybrid Deep Learning Model for Link Dynamic Vehicle Count Forecasting with Bayesian Optimization
The link dynamic vehicle count is a spatial variable that measures the traffic state of road sections, which reflects the actual traffic demand. This paper presents a hybrid deep learning method that combines the gated recurrent unit (GRU) neural network model with automatic hyperparameter tuning ba...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/5070504 |
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| _version_ | 1849699708689186816 |
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| author | Chunguang He Dianhai Wang Yi Yu Zhengyi Cai |
| author_facet | Chunguang He Dianhai Wang Yi Yu Zhengyi Cai |
| author_sort | Chunguang He |
| collection | DOAJ |
| description | The link dynamic vehicle count is a spatial variable that measures the traffic state of road sections, which reflects the actual traffic demand. This paper presents a hybrid deep learning method that combines the gated recurrent unit (GRU) neural network model with automatic hyperparameter tuning based on Bayesian optimization (BO) and the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) model. There are four steps in this hybrid approach. First, the ICEEMDAN is employed to decompose the link dynamic vehicle count time series data into several intrinsic components. Second, the components are predicted by the GRU model. At the same time, the Bayesian optimization method is utilized to automatically optimize the hyperparameters of the GRU model. Finally, the predicted subcomponents are reconstructed to obtain the final prediction results. The proposed hybrid deep learning method is tested on two roads of Hangzhou, China. Results show that, compared with the 12 benchmark models, the proposed hybrid deep learning model achieves the best performance in link dynamic vehicle count forecasting. |
| format | Article |
| id | doaj-art-3370fbcc9345408ba2bc0e699a4817fb |
| institution | DOAJ |
| issn | 2042-3195 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-3370fbcc9345408ba2bc0e699a4817fb2025-08-20T03:18:31ZengWileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/5070504A Hybrid Deep Learning Model for Link Dynamic Vehicle Count Forecasting with Bayesian OptimizationChunguang He0Dianhai Wang1Yi Yu2Zhengyi Cai3College of Civil Engineering and ArchitectureCollege of Civil Engineering and ArchitectureCollege of Civil Engineering and ArchitectureCollege of Civil Engineering and ArchitectureThe link dynamic vehicle count is a spatial variable that measures the traffic state of road sections, which reflects the actual traffic demand. This paper presents a hybrid deep learning method that combines the gated recurrent unit (GRU) neural network model with automatic hyperparameter tuning based on Bayesian optimization (BO) and the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) model. There are four steps in this hybrid approach. First, the ICEEMDAN is employed to decompose the link dynamic vehicle count time series data into several intrinsic components. Second, the components are predicted by the GRU model. At the same time, the Bayesian optimization method is utilized to automatically optimize the hyperparameters of the GRU model. Finally, the predicted subcomponents are reconstructed to obtain the final prediction results. The proposed hybrid deep learning method is tested on two roads of Hangzhou, China. Results show that, compared with the 12 benchmark models, the proposed hybrid deep learning model achieves the best performance in link dynamic vehicle count forecasting.http://dx.doi.org/10.1155/2023/5070504 |
| spellingShingle | Chunguang He Dianhai Wang Yi Yu Zhengyi Cai A Hybrid Deep Learning Model for Link Dynamic Vehicle Count Forecasting with Bayesian Optimization Journal of Advanced Transportation |
| title | A Hybrid Deep Learning Model for Link Dynamic Vehicle Count Forecasting with Bayesian Optimization |
| title_full | A Hybrid Deep Learning Model for Link Dynamic Vehicle Count Forecasting with Bayesian Optimization |
| title_fullStr | A Hybrid Deep Learning Model for Link Dynamic Vehicle Count Forecasting with Bayesian Optimization |
| title_full_unstemmed | A Hybrid Deep Learning Model for Link Dynamic Vehicle Count Forecasting with Bayesian Optimization |
| title_short | A Hybrid Deep Learning Model for Link Dynamic Vehicle Count Forecasting with Bayesian Optimization |
| title_sort | hybrid deep learning model for link dynamic vehicle count forecasting with bayesian optimization |
| url | http://dx.doi.org/10.1155/2023/5070504 |
| work_keys_str_mv | AT chunguanghe ahybriddeeplearningmodelforlinkdynamicvehiclecountforecastingwithbayesianoptimization AT dianhaiwang ahybriddeeplearningmodelforlinkdynamicvehiclecountforecastingwithbayesianoptimization AT yiyu ahybriddeeplearningmodelforlinkdynamicvehiclecountforecastingwithbayesianoptimization AT zhengyicai ahybriddeeplearningmodelforlinkdynamicvehiclecountforecastingwithbayesianoptimization AT chunguanghe hybriddeeplearningmodelforlinkdynamicvehiclecountforecastingwithbayesianoptimization AT dianhaiwang hybriddeeplearningmodelforlinkdynamicvehiclecountforecastingwithbayesianoptimization AT yiyu hybriddeeplearningmodelforlinkdynamicvehiclecountforecastingwithbayesianoptimization AT zhengyicai hybriddeeplearningmodelforlinkdynamicvehiclecountforecastingwithbayesianoptimization |