Highway Traffic Flow Prediction Algorithm Based on Multiscale Transformation and Convolutional Networks
In order to solve the problem that the traditional long-term high-speed traffic forecasting algorithm is affected by the approximation ability of the function and easy to fall into the local mass value, we wrote a multivariate-based highway traffic forecasting algorithm scaling and convolutional net...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/8427237 |
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| _version_ | 1850177262136066048 |
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| author | Yuzhu Luo Jiarong Wang Ming Wei |
| author_facet | Yuzhu Luo Jiarong Wang Ming Wei |
| author_sort | Yuzhu Luo |
| collection | DOAJ |
| description | In order to solve the problem that the traditional long-term high-speed traffic forecasting algorithm is affected by the approximation ability of the function and easy to fall into the local mass value, we wrote a multivariate-based highway traffic forecasting algorithm scaling and convolutional networks. Because the feedforward wavelet neural network algorithm predicts the short-term traffic flow in different areas, it is necessary to examine the ability to predict the difference between different models. From the standard feedforward wavelet neural network algorithm using global optimization capabilities, we improve the wolf pack algorithm, improve the search accuracy of the algorithm, get the best solution of the estimated value of the work according to the search results when completing the research objectives, and get the ability to predict the work of the model. Feedforward neural network algorithm: we develop and obtain the best short-term high-speed traffic forecast values. The results are as follows: after using the author’s algorithm, the processing time increases by 1.5 seconds, but the average percentage of errors decreases by more than 50%, in fact the error and the root mean square error decreased by about 30%, and the smoothing coefficient increased by about 1%. The prediction of the author’s algorithm for short-term high-speed traffic is better than the wavelet neural network prediction algorithm, and the prediction accuracy and stability of the author’s algorithm are higher. |
| format | Article |
| id | doaj-art-c26e146f849f4da5a86aed0a931e1a11 |
| institution | OA Journals |
| issn | 1687-5257 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-c26e146f849f4da5a86aed0a931e1a112025-08-20T02:19:02ZengWileyJournal of Control Science and Engineering1687-52572022-01-01202210.1155/2022/8427237Highway Traffic Flow Prediction Algorithm Based on Multiscale Transformation and Convolutional NetworksYuzhu Luo0Jiarong Wang1Ming Wei2Architecture DepartmentArchitecture DepartmentSchool of Journalism and CommunicationIn order to solve the problem that the traditional long-term high-speed traffic forecasting algorithm is affected by the approximation ability of the function and easy to fall into the local mass value, we wrote a multivariate-based highway traffic forecasting algorithm scaling and convolutional networks. Because the feedforward wavelet neural network algorithm predicts the short-term traffic flow in different areas, it is necessary to examine the ability to predict the difference between different models. From the standard feedforward wavelet neural network algorithm using global optimization capabilities, we improve the wolf pack algorithm, improve the search accuracy of the algorithm, get the best solution of the estimated value of the work according to the search results when completing the research objectives, and get the ability to predict the work of the model. Feedforward neural network algorithm: we develop and obtain the best short-term high-speed traffic forecast values. The results are as follows: after using the author’s algorithm, the processing time increases by 1.5 seconds, but the average percentage of errors decreases by more than 50%, in fact the error and the root mean square error decreased by about 30%, and the smoothing coefficient increased by about 1%. The prediction of the author’s algorithm for short-term high-speed traffic is better than the wavelet neural network prediction algorithm, and the prediction accuracy and stability of the author’s algorithm are higher.http://dx.doi.org/10.1155/2022/8427237 |
| spellingShingle | Yuzhu Luo Jiarong Wang Ming Wei Highway Traffic Flow Prediction Algorithm Based on Multiscale Transformation and Convolutional Networks Journal of Control Science and Engineering |
| title | Highway Traffic Flow Prediction Algorithm Based on Multiscale Transformation and Convolutional Networks |
| title_full | Highway Traffic Flow Prediction Algorithm Based on Multiscale Transformation and Convolutional Networks |
| title_fullStr | Highway Traffic Flow Prediction Algorithm Based on Multiscale Transformation and Convolutional Networks |
| title_full_unstemmed | Highway Traffic Flow Prediction Algorithm Based on Multiscale Transformation and Convolutional Networks |
| title_short | Highway Traffic Flow Prediction Algorithm Based on Multiscale Transformation and Convolutional Networks |
| title_sort | highway traffic flow prediction algorithm based on multiscale transformation and convolutional networks |
| url | http://dx.doi.org/10.1155/2022/8427237 |
| work_keys_str_mv | AT yuzhuluo highwaytrafficflowpredictionalgorithmbasedonmultiscaletransformationandconvolutionalnetworks AT jiarongwang highwaytrafficflowpredictionalgorithmbasedonmultiscaletransformationandconvolutionalnetworks AT mingwei highwaytrafficflowpredictionalgorithmbasedonmultiscaletransformationandconvolutionalnetworks |