Adaptive Coordinated Variable Speed Limit between Highway Mainline and On-Ramp with Deep Reinforcement Learning
Highway merging bottleneck is challenged with serious traffic conflicts between on-ramp and mainline vehicles, causing significant capacity drop and drastic speed changes. The paper proposes an adaptive coordinated variable speed limit model to manage highway speed of on-ramp and mainline continuous...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/2435643 |
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| _version_ | 1832566839945723904 |
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| author | Ming Cheng Chenghao Zhang Hui Jin Ziming Wang Xiaoguang Yang |
| author_facet | Ming Cheng Chenghao Zhang Hui Jin Ziming Wang Xiaoguang Yang |
| author_sort | Ming Cheng |
| collection | DOAJ |
| description | Highway merging bottleneck is challenged with serious traffic conflicts between on-ramp and mainline vehicles, causing significant capacity drop and drastic speed changes. The paper proposes an adaptive coordinated variable speed limit model to manage highway speed of on-ramp and mainline continuous sections without priority to mainline. That helps to remove the speed difference between the vehicles from on-ramp and mainline flooding into the merging zone, and to sustain actual traffic density close to critical density to counteract capacity drop as indicated with macroscopic fundamental diagram. The method of deep reinforcement learning based on deep deterministic policy gradient is employed to solve the proposed model with a row of continuous control variables. Simulation platform with VISSIM 5.3 is established, and the proposed method can enhance traffic flow through the merging zone by around 10% and 19% under static and dynamic demand, respectively, in addition to reduced density and speed variation by around 30%. This research provides insights into the management of highway capacity so as to secure traffic efficiency and reliability for the merging zone. |
| format | Article |
| id | doaj-art-907bd9ecae1343eab5fe88048b020750 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-907bd9ecae1343eab5fe88048b0207502025-02-03T01:03:08ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/2435643Adaptive Coordinated Variable Speed Limit between Highway Mainline and On-Ramp with Deep Reinforcement LearningMing Cheng0Chenghao Zhang1Hui Jin2Ziming Wang3Xiaoguang Yang4School of Rail TransportationSchool of Rail TransportationSchool of Rail TransportationSchool of Rail TransportationKey Laboratory of Road and Traffic Engineering of the Ministry of EducationHighway merging bottleneck is challenged with serious traffic conflicts between on-ramp and mainline vehicles, causing significant capacity drop and drastic speed changes. The paper proposes an adaptive coordinated variable speed limit model to manage highway speed of on-ramp and mainline continuous sections without priority to mainline. That helps to remove the speed difference between the vehicles from on-ramp and mainline flooding into the merging zone, and to sustain actual traffic density close to critical density to counteract capacity drop as indicated with macroscopic fundamental diagram. The method of deep reinforcement learning based on deep deterministic policy gradient is employed to solve the proposed model with a row of continuous control variables. Simulation platform with VISSIM 5.3 is established, and the proposed method can enhance traffic flow through the merging zone by around 10% and 19% under static and dynamic demand, respectively, in addition to reduced density and speed variation by around 30%. This research provides insights into the management of highway capacity so as to secure traffic efficiency and reliability for the merging zone.http://dx.doi.org/10.1155/2022/2435643 |
| spellingShingle | Ming Cheng Chenghao Zhang Hui Jin Ziming Wang Xiaoguang Yang Adaptive Coordinated Variable Speed Limit between Highway Mainline and On-Ramp with Deep Reinforcement Learning Journal of Advanced Transportation |
| title | Adaptive Coordinated Variable Speed Limit between Highway Mainline and On-Ramp with Deep Reinforcement Learning |
| title_full | Adaptive Coordinated Variable Speed Limit between Highway Mainline and On-Ramp with Deep Reinforcement Learning |
| title_fullStr | Adaptive Coordinated Variable Speed Limit between Highway Mainline and On-Ramp with Deep Reinforcement Learning |
| title_full_unstemmed | Adaptive Coordinated Variable Speed Limit between Highway Mainline and On-Ramp with Deep Reinforcement Learning |
| title_short | Adaptive Coordinated Variable Speed Limit between Highway Mainline and On-Ramp with Deep Reinforcement Learning |
| title_sort | adaptive coordinated variable speed limit between highway mainline and on ramp with deep reinforcement learning |
| url | http://dx.doi.org/10.1155/2022/2435643 |
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