A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data
Ramp metering that uses traffic signals to regulate vehicle flows from the on-ramps has been widely implemented to improve vehicle mobility of the freeway. Previous studies generally update signal timings in real-time based on predefined traffic measurements collected by point detectors, such as tra...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/6669028 |
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| _version_ | 1832568474591821824 |
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| author | Bing Liu Yu Tang Yuxiong Ji Yu Shen Yuchuan Du |
| author_facet | Bing Liu Yu Tang Yuxiong Ji Yu Shen Yuchuan Du |
| author_sort | Bing Liu |
| collection | DOAJ |
| description | Ramp metering that uses traffic signals to regulate vehicle flows from the on-ramps has been widely implemented to improve vehicle mobility of the freeway. Previous studies generally update signal timings in real-time based on predefined traffic measurements collected by point detectors, such as traffic volumes and occupancies. Comparing with point detectors, traffic cameras—which have been increasingly deployed on road networks—could cover larger areas and provide more detailed traffic information. In this work, we propose a deep reinforcement learning (DRL) method to explore the potential of traffic video data in improving the efficiency of ramp metering. Vehicle locations are extracted from the traffic video frames and are reformed as position matrices. The proposed method takes the preprocessed video data as inputs and learns the optimal control strategies directly from the high-dimensional inputs. A series of simulation experiments based on real-world traffic data are conducted to evaluate the proposed approach. The results demonstrate that, in comparison with a state-of-the-practice method, the proposed DRL method results in (1) lower travel times in the mainline, (2) shorter vehicle queues at the on-ramp, and (3) higher traffic flows downstream of the merging area. The results suggest that the proposed method is able to extract useful information from the video data for better ramp metering controls. |
| format | Article |
| id | doaj-art-0baf461d79f44bb9a8d2b843de871cb9 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-0baf461d79f44bb9a8d2b843de871cb92025-02-03T00:58:51ZengWileyJournal of Advanced Transportation2042-31952021-01-01202110.1155/2021/6669028A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video DataBing Liu0Yu Tang1Yuxiong Ji2Yu Shen3Yuchuan Du4Key Laboratory of Road and Traffic Engineering of the Ministry of EducationTandon School of EngineeringKey Laboratory of Road and Traffic Engineering of the Ministry of EducationKey Laboratory of Road and Traffic Engineering of the Ministry of EducationKey Laboratory of Road and Traffic Engineering of the Ministry of EducationRamp metering that uses traffic signals to regulate vehicle flows from the on-ramps has been widely implemented to improve vehicle mobility of the freeway. Previous studies generally update signal timings in real-time based on predefined traffic measurements collected by point detectors, such as traffic volumes and occupancies. Comparing with point detectors, traffic cameras—which have been increasingly deployed on road networks—could cover larger areas and provide more detailed traffic information. In this work, we propose a deep reinforcement learning (DRL) method to explore the potential of traffic video data in improving the efficiency of ramp metering. Vehicle locations are extracted from the traffic video frames and are reformed as position matrices. The proposed method takes the preprocessed video data as inputs and learns the optimal control strategies directly from the high-dimensional inputs. A series of simulation experiments based on real-world traffic data are conducted to evaluate the proposed approach. The results demonstrate that, in comparison with a state-of-the-practice method, the proposed DRL method results in (1) lower travel times in the mainline, (2) shorter vehicle queues at the on-ramp, and (3) higher traffic flows downstream of the merging area. The results suggest that the proposed method is able to extract useful information from the video data for better ramp metering controls.http://dx.doi.org/10.1155/2021/6669028 |
| spellingShingle | Bing Liu Yu Tang Yuxiong Ji Yu Shen Yuchuan Du A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data Journal of Advanced Transportation |
| title | A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data |
| title_full | A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data |
| title_fullStr | A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data |
| title_full_unstemmed | A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data |
| title_short | A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data |
| title_sort | deep reinforcement learning approach for ramp metering based on traffic video data |
| url | http://dx.doi.org/10.1155/2021/6669028 |
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