Traffic Status Prediction of Arterial Roads Based on the Deep Recurrent Q-Learning
With the exponential growth of traffic data and the complexity of traffic conditions, in order to effectively store and analyse data to feed back valid information, this paper proposed an urban road traffic status prediction model based on the optimized deep recurrent Q-Learning method. The model is...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8831521 |
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| _version_ | 1832560250657439744 |
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| author | Wei Hao Donglei Rong Kefu Yi Qiang Zeng Zhibo Gao Wenguang Wu Chongfeng Wei Biljana Scepanovic |
| author_facet | Wei Hao Donglei Rong Kefu Yi Qiang Zeng Zhibo Gao Wenguang Wu Chongfeng Wei Biljana Scepanovic |
| author_sort | Wei Hao |
| collection | DOAJ |
| description | With the exponential growth of traffic data and the complexity of traffic conditions, in order to effectively store and analyse data to feed back valid information, this paper proposed an urban road traffic status prediction model based on the optimized deep recurrent Q-Learning method. The model is based on the optimized Long Short-Term Memory (LSTM) algorithm to handle the explosive growth of Q-table data, which not only avoids the gradient explosion and disappearance but also has the efficient storage and analysis. The continuous training and memory storage of the training sets are used to improve the system sensitivity, and then, the test sets are predicted based on the accumulated experience pool to obtain high-precision prediction results. The traffic flow data from Wanjiali Road to Shuangtang Road in Changsha City are tested as a case. The research results show that the prediction of the traffic delay index is within a reasonable interval, and it is significantly better than traditional prediction methods such as the LSTM, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), exponential smoothing method, and Back Propagation (BP) neural network, which shows that the model proposed in this paper has the feasibility of application. |
| format | Article |
| id | doaj-art-736c344957f247d0973c681be8721f16 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-736c344957f247d0973c681be8721f162025-02-03T01:27:59ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/88315218831521Traffic Status Prediction of Arterial Roads Based on the Deep Recurrent Q-LearningWei Hao0Donglei Rong1Kefu Yi2Qiang Zeng3Zhibo Gao4Wenguang Wu5Chongfeng Wei6Biljana Scepanovic7Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science and Technology, Changsha, Hunan 410205, ChinaHunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science and Technology, Changsha, Hunan 410205, ChinaSchool of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, ChinaSchool of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, ChinaKey Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai, ChinaSchool of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, ChinaMechanical and Construction Engineering, Northumbria University, Ellison Place, Newcastle upon Tyne NE1 8ST, UKFaculty of Civil Engineering, University of Montenegro, 81000 Podgorica, MontenegroWith the exponential growth of traffic data and the complexity of traffic conditions, in order to effectively store and analyse data to feed back valid information, this paper proposed an urban road traffic status prediction model based on the optimized deep recurrent Q-Learning method. The model is based on the optimized Long Short-Term Memory (LSTM) algorithm to handle the explosive growth of Q-table data, which not only avoids the gradient explosion and disappearance but also has the efficient storage and analysis. The continuous training and memory storage of the training sets are used to improve the system sensitivity, and then, the test sets are predicted based on the accumulated experience pool to obtain high-precision prediction results. The traffic flow data from Wanjiali Road to Shuangtang Road in Changsha City are tested as a case. The research results show that the prediction of the traffic delay index is within a reasonable interval, and it is significantly better than traditional prediction methods such as the LSTM, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), exponential smoothing method, and Back Propagation (BP) neural network, which shows that the model proposed in this paper has the feasibility of application.http://dx.doi.org/10.1155/2020/8831521 |
| spellingShingle | Wei Hao Donglei Rong Kefu Yi Qiang Zeng Zhibo Gao Wenguang Wu Chongfeng Wei Biljana Scepanovic Traffic Status Prediction of Arterial Roads Based on the Deep Recurrent Q-Learning Journal of Advanced Transportation |
| title | Traffic Status Prediction of Arterial Roads Based on the Deep Recurrent Q-Learning |
| title_full | Traffic Status Prediction of Arterial Roads Based on the Deep Recurrent Q-Learning |
| title_fullStr | Traffic Status Prediction of Arterial Roads Based on the Deep Recurrent Q-Learning |
| title_full_unstemmed | Traffic Status Prediction of Arterial Roads Based on the Deep Recurrent Q-Learning |
| title_short | Traffic Status Prediction of Arterial Roads Based on the Deep Recurrent Q-Learning |
| title_sort | traffic status prediction of arterial roads based on the deep recurrent q learning |
| url | http://dx.doi.org/10.1155/2020/8831521 |
| work_keys_str_mv | AT weihao trafficstatuspredictionofarterialroadsbasedonthedeeprecurrentqlearning AT dongleirong trafficstatuspredictionofarterialroadsbasedonthedeeprecurrentqlearning AT kefuyi trafficstatuspredictionofarterialroadsbasedonthedeeprecurrentqlearning AT qiangzeng trafficstatuspredictionofarterialroadsbasedonthedeeprecurrentqlearning AT zhibogao trafficstatuspredictionofarterialroadsbasedonthedeeprecurrentqlearning AT wenguangwu trafficstatuspredictionofarterialroadsbasedonthedeeprecurrentqlearning AT chongfengwei trafficstatuspredictionofarterialroadsbasedonthedeeprecurrentqlearning AT biljanascepanovic trafficstatuspredictionofarterialroadsbasedonthedeeprecurrentqlearning |