A Two-Stage Decomposition-Reinforcement Learning Optimal Combined Short-Time Traffic Flow Prediction Model Considering Multiple Factors
Accurate short-term traffic flow prediction is a prerequisite for achieving an intelligent transportation system to proactively alleviate traffic congestion. Considering the complex and variable traffic environment, so that the traffic flow contains a large number of non-linear characteristics, whic...
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| Format: | Article |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/16/7978 |
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| author | Dayi Qu Kun Chen Shaojie Wang Qikun Wang |
| author_facet | Dayi Qu Kun Chen Shaojie Wang Qikun Wang |
| author_sort | Dayi Qu |
| collection | DOAJ |
| description | Accurate short-term traffic flow prediction is a prerequisite for achieving an intelligent transportation system to proactively alleviate traffic congestion. Considering the complex and variable traffic environment, so that the traffic flow contains a large number of non-linear characteristics, which makes it difficult to improve the prediction accuracy, a combined prediction model that reduces the unsteadiness of traffic flow and fully extracts the traffic flow features is proposed. Firstly, decompose the traffic flow data into multiple components by the seasonal and trend decomposition using loess (STL); these components contain different features, and the optimized variational modal decomposition (VMD) is used for the second decomposition of the component with large fluctuation frequencies, and then the components are reconstructed according to the fuzzy entropy and Lempel-Ziv complexity index and the Pearson correlation coefficient is used to filter the traffic flow features. Then light gradient boosting machine (LightGBM), long short-term memory with attention mechanism (LA), and kernel extreme learning machine with genetic algorithm optimization (GA-KELM) are built for prediction. Finally, we use reinforcement learning to integrate the advantages of each model, and the weights of each model are determined to obtain the best prediction results. The case study shows that the model established in this paper is better than other models in predicting urban road traffic flow, with an average absolute error of 2.622 and a root mean square error of 3.479, both of which are lower than the prediction errors of other models, indicating that the model can fully extract the features in complex traffic flow. |
| first_indexed | 2024-03-09T04:44:46Z |
| format | Article |
| id | doaj.art-444931b29d6c46dda420894e082d94cf |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T04:44:46Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-444931b29d6c46dda420894e082d94cf2023-12-03T13:16:32ZengMDPI AGApplied Sciences2076-34172022-08-011216797810.3390/app12167978A Two-Stage Decomposition-Reinforcement Learning Optimal Combined Short-Time Traffic Flow Prediction Model Considering Multiple FactorsDayi Qu0Kun Chen1Shaojie Wang2Qikun Wang3School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266525, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266525, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266525, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266525, ChinaAccurate short-term traffic flow prediction is a prerequisite for achieving an intelligent transportation system to proactively alleviate traffic congestion. Considering the complex and variable traffic environment, so that the traffic flow contains a large number of non-linear characteristics, which makes it difficult to improve the prediction accuracy, a combined prediction model that reduces the unsteadiness of traffic flow and fully extracts the traffic flow features is proposed. Firstly, decompose the traffic flow data into multiple components by the seasonal and trend decomposition using loess (STL); these components contain different features, and the optimized variational modal decomposition (VMD) is used for the second decomposition of the component with large fluctuation frequencies, and then the components are reconstructed according to the fuzzy entropy and Lempel-Ziv complexity index and the Pearson correlation coefficient is used to filter the traffic flow features. Then light gradient boosting machine (LightGBM), long short-term memory with attention mechanism (LA), and kernel extreme learning machine with genetic algorithm optimization (GA-KELM) are built for prediction. Finally, we use reinforcement learning to integrate the advantages of each model, and the weights of each model are determined to obtain the best prediction results. The case study shows that the model established in this paper is better than other models in predicting urban road traffic flow, with an average absolute error of 2.622 and a root mean square error of 3.479, both of which are lower than the prediction errors of other models, indicating that the model can fully extract the features in complex traffic flow.https://www.mdpi.com/2076-3417/12/16/7978short-term traffic flow predictiontwo-level decompositionfeature selectionreinforcement learningcombinatorial models |
| spellingShingle | Dayi Qu Kun Chen Shaojie Wang Qikun Wang A Two-Stage Decomposition-Reinforcement Learning Optimal Combined Short-Time Traffic Flow Prediction Model Considering Multiple Factors Applied Sciences short-term traffic flow prediction two-level decomposition feature selection reinforcement learning combinatorial models |
| title | A Two-Stage Decomposition-Reinforcement Learning Optimal Combined Short-Time Traffic Flow Prediction Model Considering Multiple Factors |
| title_full | A Two-Stage Decomposition-Reinforcement Learning Optimal Combined Short-Time Traffic Flow Prediction Model Considering Multiple Factors |
| title_fullStr | A Two-Stage Decomposition-Reinforcement Learning Optimal Combined Short-Time Traffic Flow Prediction Model Considering Multiple Factors |
| title_full_unstemmed | A Two-Stage Decomposition-Reinforcement Learning Optimal Combined Short-Time Traffic Flow Prediction Model Considering Multiple Factors |
| title_short | A Two-Stage Decomposition-Reinforcement Learning Optimal Combined Short-Time Traffic Flow Prediction Model Considering Multiple Factors |
| title_sort | two stage decomposition reinforcement learning optimal combined short time traffic flow prediction model considering multiple factors |
| topic | short-term traffic flow prediction two-level decomposition feature selection reinforcement learning combinatorial models |
| url | https://www.mdpi.com/2076-3417/12/16/7978 |
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