Highway Traffic Speed Prediction in Rainy Environment Based on APSO-GRU
In order to accurately analyse the impact of the rainy environment on the characteristics of highway traffic flow, a short-term traffic flow speed prediction model based on gate recurrent unit (GRU) and adaptive nonlinear inertia weight particle swarm optimization (APSO) was proposed. Firstly, the r...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/4060740 |
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| _version_ | 1832566294169255936 |
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| author | Dongqing Han Xin Yang Guang Li Shuangyin Wang Zhen Wang Jiandong Zhao |
| author_facet | Dongqing Han Xin Yang Guang Li Shuangyin Wang Zhen Wang Jiandong Zhao |
| author_sort | Dongqing Han |
| collection | DOAJ |
| description | In order to accurately analyse the impact of the rainy environment on the characteristics of highway traffic flow, a short-term traffic flow speed prediction model based on gate recurrent unit (GRU) and adaptive nonlinear inertia weight particle swarm optimization (APSO) was proposed. Firstly, the rainfall and highway traffic flow data were cleaned, and then they are matched according to the spatiotemporal relationship. Secondly, through the method of multivariate analysis of variance, the significance of the impact of potential factors on traffic flow speed was explored. Then, a GRU-based traffic flow speed prediction model in rainy environment is proposed, and the actual road sections under different rainfall scenarios were verified. After that, in view of the problem that the prediction accuracy of the GRU model was low in the continuous rainfall scenario, the APSO algorithm was used to optimize the parameters of the GRU network, and the APSO-GRU prediction model was constructed and verifications under the same road section and rain scene were carried out. The results show that the APSO-GRU model has significantly improved prediction stability than the GRU model and can better extract rainfall features during continuous rainfall, with an average prediction accuracy rate of 96.74%. |
| format | Article |
| id | doaj-art-ae25c1fa771248a082862a0c2c606d59 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-ae25c1fa771248a082862a0c2c606d592025-02-03T01:04:32ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/40607404060740Highway Traffic Speed Prediction in Rainy Environment Based on APSO-GRUDongqing Han0Xin Yang1Guang Li2Shuangyin Wang3Zhen Wang4Jiandong Zhao5Zhong Dian Jian Ji Jiao Highway Investment Development Company Limited, Shijiazhuang, Hebei 050090, ChinaZhong Dian Jian Ji Jiao Highway Investment Development Company Limited, Shijiazhuang, Hebei 050090, ChinaHebei Intelligent Transportation Technology Co., Ltd of HEBTIG, Shijiazhuang, Hebei 050090, ChinaZhong Dian Jian Ji Jiao Highway Investment Development Company Limited, Shijiazhuang, Hebei 050090, ChinaSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, ChinaIn order to accurately analyse the impact of the rainy environment on the characteristics of highway traffic flow, a short-term traffic flow speed prediction model based on gate recurrent unit (GRU) and adaptive nonlinear inertia weight particle swarm optimization (APSO) was proposed. Firstly, the rainfall and highway traffic flow data were cleaned, and then they are matched according to the spatiotemporal relationship. Secondly, through the method of multivariate analysis of variance, the significance of the impact of potential factors on traffic flow speed was explored. Then, a GRU-based traffic flow speed prediction model in rainy environment is proposed, and the actual road sections under different rainfall scenarios were verified. After that, in view of the problem that the prediction accuracy of the GRU model was low in the continuous rainfall scenario, the APSO algorithm was used to optimize the parameters of the GRU network, and the APSO-GRU prediction model was constructed and verifications under the same road section and rain scene were carried out. The results show that the APSO-GRU model has significantly improved prediction stability than the GRU model and can better extract rainfall features during continuous rainfall, with an average prediction accuracy rate of 96.74%.http://dx.doi.org/10.1155/2021/4060740 |
| spellingShingle | Dongqing Han Xin Yang Guang Li Shuangyin Wang Zhen Wang Jiandong Zhao Highway Traffic Speed Prediction in Rainy Environment Based on APSO-GRU Journal of Advanced Transportation |
| title | Highway Traffic Speed Prediction in Rainy Environment Based on APSO-GRU |
| title_full | Highway Traffic Speed Prediction in Rainy Environment Based on APSO-GRU |
| title_fullStr | Highway Traffic Speed Prediction in Rainy Environment Based on APSO-GRU |
| title_full_unstemmed | Highway Traffic Speed Prediction in Rainy Environment Based on APSO-GRU |
| title_short | Highway Traffic Speed Prediction in Rainy Environment Based on APSO-GRU |
| title_sort | highway traffic speed prediction in rainy environment based on apso gru |
| url | http://dx.doi.org/10.1155/2021/4060740 |
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