Design of reinforcement learning for perimeter control using network transmission model based macroscopic traffic simulation

Cited 1 time in webofscience Cited 0 time in scopus
  • Hit : 169
  • Download : 129
DC FieldValueLanguage
dc.contributor.authorYoon, Jinwonko
dc.contributor.authorKim, Sunghoonko
dc.contributor.authorByon, Young-Jiko
dc.contributor.authorYeo, Hwasooko
dc.date.accessioned2020-09-18T04:01:28Z-
dc.date.available2020-09-18T04:01:28Z-
dc.date.created2020-08-24-
dc.date.issued2020-07-
dc.identifier.citationPLOS ONE, v.15, no.7-
dc.identifier.issn1932-6203-
dc.identifier.urihttp://hdl.handle.net/10203/276109-
dc.description.abstractPerimeter 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.-
dc.languageEnglish-
dc.publisherPUBLIC LIBRARY SCIENCE-
dc.titleDesign of reinforcement learning for perimeter control using network transmission model based macroscopic traffic simulation-
dc.typeArticle-
dc.identifier.wosid000556884700059-
dc.identifier.scopusid2-s2.0-85088885648-
dc.type.rimsART-
dc.citation.volume15-
dc.citation.issue7-
dc.citation.publicationnamePLOS ONE-
dc.identifier.doi10.1371/journal.pone.0236655-
dc.contributor.localauthorYeo, Hwasoo-
dc.contributor.nonIdAuthorKim, Sunghoon-
dc.contributor.nonIdAuthorByon, Young-Ji-
dc.description.isOpenAccessY-
dc.type.journalArticleArticle-
dc.subject.keywordPlusFUNDAMENTAL DIAGRAM-
dc.subject.keywordPlusURBAN NETWORKS-
dc.subject.keywordPlusSHIP TRACKING-
dc.subject.keywordPlusFLOW-
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 1 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0