An Improved Traffic Congestion Monitoring System Based on Federated Learning
This study introduces a software-based traffic congestion monitoring system. The transportation system controls the traffic between cities all over the world. Traffic congestion happens not only in cities, but also on highways and other places. The current transportation system is not satisfactory i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/11/7/365 |
| _version_ | 1797562247345602560 |
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| author | Chenming Xu Yunlong Mao |
| author_facet | Chenming Xu Yunlong Mao |
| author_sort | Chenming Xu |
| collection | DOAJ |
| description | This study introduces a software-based traffic congestion monitoring system. The transportation system controls the traffic between cities all over the world. Traffic congestion happens not only in cities, but also on highways and other places. The current transportation system is not satisfactory in the area without monitoring. In order to improve the limitations of the current traffic system in obtaining road data and expand its visual range, the system uses remote sensing data as the data source for judging congestion. Since some remote sensing data needs to be kept confidential, this is a problem to be solved to effectively protect the safety of remote sensing data during the deep learning training process. Compared with the general deep learning training method, this study provides a federated learning method to identify vehicle targets in remote sensing images to solve the problem of data privacy in the training process of remote sensing data. The experiment takes the remote sensing image data sets of Los Angeles Road and Washington Road as samples for training, and the training results can achieve an accuracy of about 85%, and the estimated processing time of each image can be as low as 0.047 s. In the final experimental results, the system can automatically identify the vehicle targets in the remote sensing images to achieve the purpose of detecting congestion. |
| first_indexed | 2024-03-10T18:26:29Z |
| format | Article |
| id | doaj.art-3172c0ec3a2841188c16fe199857707e |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T18:26:29Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-3172c0ec3a2841188c16fe199857707e2023-11-20T06:59:09ZengMDPI AGInformation2078-24892020-07-0111736510.3390/info11070365An Improved Traffic Congestion Monitoring System Based on Federated LearningChenming Xu0Yunlong Mao1Department of Computer Application, China University of Geosciences, Wuhan 430074, ChinaDepartment of Computer Science and Technology, Nanjing University, Nanjing 210023, ChinaThis study introduces a software-based traffic congestion monitoring system. The transportation system controls the traffic between cities all over the world. Traffic congestion happens not only in cities, but also on highways and other places. The current transportation system is not satisfactory in the area without monitoring. In order to improve the limitations of the current traffic system in obtaining road data and expand its visual range, the system uses remote sensing data as the data source for judging congestion. Since some remote sensing data needs to be kept confidential, this is a problem to be solved to effectively protect the safety of remote sensing data during the deep learning training process. Compared with the general deep learning training method, this study provides a federated learning method to identify vehicle targets in remote sensing images to solve the problem of data privacy in the training process of remote sensing data. The experiment takes the remote sensing image data sets of Los Angeles Road and Washington Road as samples for training, and the training results can achieve an accuracy of about 85%, and the estimated processing time of each image can be as low as 0.047 s. In the final experimental results, the system can automatically identify the vehicle targets in the remote sensing images to achieve the purpose of detecting congestion.https://www.mdpi.com/2078-2489/11/7/365federated learningremote sensingtransportation systemtraffic congestion monitoring systemPaddlePaddle |
| spellingShingle | Chenming Xu Yunlong Mao An Improved Traffic Congestion Monitoring System Based on Federated Learning Information federated learning remote sensing transportation system traffic congestion monitoring system PaddlePaddle |
| title | An Improved Traffic Congestion Monitoring System Based on Federated Learning |
| title_full | An Improved Traffic Congestion Monitoring System Based on Federated Learning |
| title_fullStr | An Improved Traffic Congestion Monitoring System Based on Federated Learning |
| title_full_unstemmed | An Improved Traffic Congestion Monitoring System Based on Federated Learning |
| title_short | An Improved Traffic Congestion Monitoring System Based on Federated Learning |
| title_sort | improved traffic congestion monitoring system based on federated learning |
| topic | federated learning remote sensing transportation system traffic congestion monitoring system PaddlePaddle |
| url | https://www.mdpi.com/2078-2489/11/7/365 |
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