A Multiscale and High-Precision LSTM-GASVR Short-Term Traffic Flow Prediction Model
Short-term traffic flow has the characteristics of complex, changeable, strong timeliness, and so on. So the traditional prediction algorithm is difficult to meet its high real-time and accuracy requirements. In this paper, a multiscale and high-precision LSTM-GASVR short-term traffic flow predictio...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/1434080 |
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| author | Jingmei Zhou Hui Chang Xin Cheng Xiangmo Zhao |
| author_facet | Jingmei Zhou Hui Chang Xin Cheng Xiangmo Zhao |
| author_sort | Jingmei Zhou |
| collection | DOAJ |
| description | Short-term traffic flow has the characteristics of complex, changeable, strong timeliness, and so on. So the traditional prediction algorithm is difficult to meet its high real-time and accuracy requirements. In this paper, a multiscale and high-precision LSTM-GASVR short-term traffic flow prediction algorithm is proposed. This method uses 15 min traffic flow data of the first 16 sections as input and completes the data preprocessing operation through reconstruction, normalization, and rising dimension by working day factor; establishing the prediction model based on the long- and short-term memory network (LSTM) and inverse normalization; and proposing the GA-SVR model to optimize the prediction results, so as to realize the real-time high-precision prediction of traffic flow. The prediction experiment is carried out according to the charge data of a toll station in Xi’an, Shaanxi Province, from May 2018 to May 2019. The comparison and analysis of various algorithms show that the prediction algorithm proposed in this paper is 20% higher than the LSTM, GRU, CNN, SAE, ARIMA, and SVR, and the R2 can reach 0.982, the explanatory variance is 0.982, and the MAPE is 0.118. The proposed traffic flow prediction algorithm provides strong support for traffic managers to judge the state of the road network to control traffic and guide traffic flow. |
| format | Article |
| id | doaj-art-b920fc2d2f4e462ca94313a20e5c36cd |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-b920fc2d2f4e462ca94313a20e5c36cd2025-02-03T01:28:35ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/14340801434080A Multiscale and High-Precision LSTM-GASVR Short-Term Traffic Flow Prediction ModelJingmei Zhou0Hui Chang1Xin Cheng2Xiangmo Zhao3School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaShort-term traffic flow has the characteristics of complex, changeable, strong timeliness, and so on. So the traditional prediction algorithm is difficult to meet its high real-time and accuracy requirements. In this paper, a multiscale and high-precision LSTM-GASVR short-term traffic flow prediction algorithm is proposed. This method uses 15 min traffic flow data of the first 16 sections as input and completes the data preprocessing operation through reconstruction, normalization, and rising dimension by working day factor; establishing the prediction model based on the long- and short-term memory network (LSTM) and inverse normalization; and proposing the GA-SVR model to optimize the prediction results, so as to realize the real-time high-precision prediction of traffic flow. The prediction experiment is carried out according to the charge data of a toll station in Xi’an, Shaanxi Province, from May 2018 to May 2019. The comparison and analysis of various algorithms show that the prediction algorithm proposed in this paper is 20% higher than the LSTM, GRU, CNN, SAE, ARIMA, and SVR, and the R2 can reach 0.982, the explanatory variance is 0.982, and the MAPE is 0.118. The proposed traffic flow prediction algorithm provides strong support for traffic managers to judge the state of the road network to control traffic and guide traffic flow.http://dx.doi.org/10.1155/2020/1434080 |
| spellingShingle | Jingmei Zhou Hui Chang Xin Cheng Xiangmo Zhao A Multiscale and High-Precision LSTM-GASVR Short-Term Traffic Flow Prediction Model Complexity |
| title | A Multiscale and High-Precision LSTM-GASVR Short-Term Traffic Flow Prediction Model |
| title_full | A Multiscale and High-Precision LSTM-GASVR Short-Term Traffic Flow Prediction Model |
| title_fullStr | A Multiscale and High-Precision LSTM-GASVR Short-Term Traffic Flow Prediction Model |
| title_full_unstemmed | A Multiscale and High-Precision LSTM-GASVR Short-Term Traffic Flow Prediction Model |
| title_short | A Multiscale and High-Precision LSTM-GASVR Short-Term Traffic Flow Prediction Model |
| title_sort | multiscale and high precision lstm gasvr short term traffic flow prediction model |
| url | http://dx.doi.org/10.1155/2020/1434080 |
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