Reinforcement Learning Ramp Metering without Complete Information
This paper develops a model of reinforcement learning ramp metering (RLRM) without complete information, which is applied to alleviate traffic congestions on ramps. RLRM consists of prediction tools depending on traffic flow simulation and optimal choice model based on reinforcement learning theorie...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2012/208456 |
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| _version_ | 1849472921881280512 |
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| author | Xing-Ju Wang Xiao-Ming Xi Gui-Feng Gao |
| author_facet | Xing-Ju Wang Xiao-Ming Xi Gui-Feng Gao |
| author_sort | Xing-Ju Wang |
| collection | DOAJ |
| description | This paper develops a model of reinforcement learning ramp metering (RLRM) without complete information, which is applied to alleviate traffic congestions on ramps. RLRM consists of prediction tools depending on traffic flow simulation and optimal choice model based on reinforcement learning theories. Moreover, it is also a dynamic process with abilities of automaticity, memory and performance feedback. Numerical cases are given in this study to demonstrate RLRM such as calculating outflow rate, density, average speed, and travel time compared to no control and fixed-time control. Results indicate that the greater is the inflow, the more is the effect. In addition, the stability of RLRM is better than fixed-time control. |
| format | Article |
| id | doaj-art-db226ffe45944a6488a67cd4d85990c2 |
| institution | Kabale University |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-db226ffe45944a6488a67cd4d85990c22025-08-20T03:24:21ZengWileyJournal of Control Science and Engineering1687-52491687-52572012-01-01201210.1155/2012/208456208456Reinforcement Learning Ramp Metering without Complete InformationXing-Ju Wang0Xiao-Ming Xi1Gui-Feng Gao2School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang, Hebei 050043, ChinaSchool of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang, Hebei 050043, ChinaSchool of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang, Hebei 050043, ChinaThis paper develops a model of reinforcement learning ramp metering (RLRM) without complete information, which is applied to alleviate traffic congestions on ramps. RLRM consists of prediction tools depending on traffic flow simulation and optimal choice model based on reinforcement learning theories. Moreover, it is also a dynamic process with abilities of automaticity, memory and performance feedback. Numerical cases are given in this study to demonstrate RLRM such as calculating outflow rate, density, average speed, and travel time compared to no control and fixed-time control. Results indicate that the greater is the inflow, the more is the effect. In addition, the stability of RLRM is better than fixed-time control.http://dx.doi.org/10.1155/2012/208456 |
| spellingShingle | Xing-Ju Wang Xiao-Ming Xi Gui-Feng Gao Reinforcement Learning Ramp Metering without Complete Information Journal of Control Science and Engineering |
| title | Reinforcement Learning Ramp Metering without Complete Information |
| title_full | Reinforcement Learning Ramp Metering without Complete Information |
| title_fullStr | Reinforcement Learning Ramp Metering without Complete Information |
| title_full_unstemmed | Reinforcement Learning Ramp Metering without Complete Information |
| title_short | Reinforcement Learning Ramp Metering without Complete Information |
| title_sort | reinforcement learning ramp metering without complete information |
| url | http://dx.doi.org/10.1155/2012/208456 |
| work_keys_str_mv | AT xingjuwang reinforcementlearningrampmeteringwithoutcompleteinformation AT xiaomingxi reinforcementlearningrampmeteringwithoutcompleteinformation AT guifenggao reinforcementlearningrampmeteringwithoutcompleteinformation |