Design of reinforcement learning for perimeter control using network transmission model based macroscopic traffic simulation.
Perimeter control is an emerging alternative for traffic signal control, which regulates the traffic flows on the periphery of a road network. Some model-based approaches have been suggested earlier for the optimization of perimeter control based on macroscopic fundamental diagrams (MFDs). However,...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0236655 |
| _version_ | 1818833403415363584 |
|---|---|
| author | Jinwon Yoon Sunghoon Kim Young-Ji Byon Hwasoo Yeo |
| author_facet | Jinwon Yoon Sunghoon Kim Young-Ji Byon Hwasoo Yeo |
| author_sort | Jinwon Yoon |
| collection | DOAJ |
| description | Perimeter control is an emerging alternative for traffic signal control, which regulates the traffic flows on the periphery of a road network. Some model-based approaches have been suggested earlier for the optimization of perimeter control based on macroscopic fundamental diagrams (MFDs). However, there are several limitations when considering their application to a large-scale urban area because the model-based approaches may not be scalable to multiple regions and inappropriate for handling various effects caused by the shape change of MFDs. Therefore, we propose a model-free and data-driven approach that combines reinforcement learning (RL) with the macroscopic traffic simulation based on the recently developed network transmission model. First, we design four perimeter control models with different macroscopic traffic variables and parametrizations. Then, we validate the proposed models by evaluating their performances with the test demand scenarios at different levels. The validation results show that the model containing travel demand information adapts to a new demand scenario better than the model containing only density-related factors. |
| first_indexed | 2024-12-19T02:18:22Z |
| format | Article |
| id | doaj.art-b045cd79ef5f496cb3bd1dea86ad8970 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-19T02:18:22Z |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-b045cd79ef5f496cb3bd1dea86ad89702022-12-21T20:40:20ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01157e023665510.1371/journal.pone.0236655Design of reinforcement learning for perimeter control using network transmission model based macroscopic traffic simulation.Jinwon YoonSunghoon KimYoung-Ji ByonHwasoo YeoPerimeter control is an emerging alternative for traffic signal control, which regulates the traffic flows on the periphery of a road network. Some model-based approaches have been suggested earlier for the optimization of perimeter control based on macroscopic fundamental diagrams (MFDs). However, there are several limitations when considering their application to a large-scale urban area because the model-based approaches may not be scalable to multiple regions and inappropriate for handling various effects caused by the shape change of MFDs. Therefore, we propose a model-free and data-driven approach that combines reinforcement learning (RL) with the macroscopic traffic simulation based on the recently developed network transmission model. First, we design four perimeter control models with different macroscopic traffic variables and parametrizations. Then, we validate the proposed models by evaluating their performances with the test demand scenarios at different levels. The validation results show that the model containing travel demand information adapts to a new demand scenario better than the model containing only density-related factors.https://doi.org/10.1371/journal.pone.0236655 |
| spellingShingle | Jinwon Yoon Sunghoon Kim Young-Ji Byon Hwasoo Yeo Design of reinforcement learning for perimeter control using network transmission model based macroscopic traffic simulation. PLoS ONE |
| title | Design of reinforcement learning for perimeter control using network transmission model based macroscopic traffic simulation. |
| title_full | Design of reinforcement learning for perimeter control using network transmission model based macroscopic traffic simulation. |
| title_fullStr | Design of reinforcement learning for perimeter control using network transmission model based macroscopic traffic simulation. |
| title_full_unstemmed | Design of reinforcement learning for perimeter control using network transmission model based macroscopic traffic simulation. |
| title_short | Design of reinforcement learning for perimeter control using network transmission model based macroscopic traffic simulation. |
| title_sort | design of reinforcement learning for perimeter control using network transmission model based macroscopic traffic simulation |
| url | https://doi.org/10.1371/journal.pone.0236655 |
| work_keys_str_mv | AT jinwonyoon designofreinforcementlearningforperimetercontrolusingnetworktransmissionmodelbasedmacroscopictrafficsimulation AT sunghoonkim designofreinforcementlearningforperimetercontrolusingnetworktransmissionmodelbasedmacroscopictrafficsimulation AT youngjibyon designofreinforcementlearningforperimetercontrolusingnetworktransmissionmodelbasedmacroscopictrafficsimulation AT hwasooyeo designofreinforcementlearningforperimetercontrolusingnetworktransmissionmodelbasedmacroscopictrafficsimulation |