To adapt or not to adapt : consequences of adapting driver and traffic light agents
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | , , |
Tipo de documento: | Capítulo de livro |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/31092 |
Resumo: | One way to cope with the increasing traffic demand is to integrate standard solutions with more intelligent control measures. However, the result of possible interferences between intelligent control or information provision tools and other components of the overall traffic system is not easily predictable. This paper discusses the effects of integrating co-adaptive decision-making regarding route choices (by drivers) and control measures (by traffic lights). The motivation behind this is that optimization of traffic light control is starting to be integrated with navigation support for drivers. We use microscopic, agent-based modelling and simulation, in opposition to the classical network analysis, as this work focuses on the effect of local adaptation. In a scenario that exhibits features comparable to real-world networks, we evaluate different types of adaptation by drivers and by traffic lights, based on local perceptions. In order to compare the performance, we have also used a global level optimization method based on genetic algorithms. |
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Bazzan, Ana Lucia CetertichOliveira, Denise deKlugl, FranziskaNagel, Kai2011-08-16T06:01:15Z2008http://hdl.handle.net/10183/31092000683625One way to cope with the increasing traffic demand is to integrate standard solutions with more intelligent control measures. However, the result of possible interferences between intelligent control or information provision tools and other components of the overall traffic system is not easily predictable. This paper discusses the effects of integrating co-adaptive decision-making regarding route choices (by drivers) and control measures (by traffic lights). The motivation behind this is that optimization of traffic light control is starting to be integrated with navigation support for drivers. We use microscopic, agent-based modelling and simulation, in opposition to the classical network analysis, as this work focuses on the effect of local adaptation. In a scenario that exhibits features comparable to real-world networks, we evaluate different types of adaptation by drivers and by traffic lights, based on local perceptions. In order to compare the performance, we have also used a global level optimization method based on genetic algorithms.application/pdfengEuropean Symposium on Adaptive and Learning Agents and Multi-Agent Systems (5. : 2005 : Paris, France) (6. : Brussel, Belgium) (7. : 2007 Apr. : Maastricht, The Netherlands). Revised Selected Papers. Berlin : Springer, 2008. p. 1-14Inteligência artificialEngenharia : TrafegoTo adapt or not to adapt : consequences of adapting driver and traffic light agentsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookPartinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSORIGINAL000683625.pdf000683625.pdfCapítulo (inglês)application/pdf352848http://www.lume.ufrgs.br/bitstream/10183/31092/1/000683625.pdf477856f6b93826fc52e2a25ec89d6be6MD51TEXT000683625.pdf.txt000683625.pdf.txtExtracted Texttext/plain40276http://www.lume.ufrgs.br/bitstream/10183/31092/2/000683625.pdf.txt16f8b051e6c15f0fb50446c186c541ceMD52THUMBNAIL000683625.pdf.jpg000683625.pdf.jpgGenerated Thumbnailimage/jpeg1706http://www.lume.ufrgs.br/bitstream/10183/31092/3/000683625.pdf.jpg26bc26fe40554d29bc2c2ee3a3d56a19MD5310183/310922023-01-05 06:04:26.315471oai:www.lume.ufrgs.br:10183/31092Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2023-01-05T08:04:26Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
To adapt or not to adapt : consequences of adapting driver and traffic light agents |
title |
To adapt or not to adapt : consequences of adapting driver and traffic light agents |
spellingShingle |
To adapt or not to adapt : consequences of adapting driver and traffic light agents Bazzan, Ana Lucia Cetertich Inteligência artificial Engenharia : Trafego |
title_short |
To adapt or not to adapt : consequences of adapting driver and traffic light agents |
title_full |
To adapt or not to adapt : consequences of adapting driver and traffic light agents |
title_fullStr |
To adapt or not to adapt : consequences of adapting driver and traffic light agents |
title_full_unstemmed |
To adapt or not to adapt : consequences of adapting driver and traffic light agents |
title_sort |
To adapt or not to adapt : consequences of adapting driver and traffic light agents |
author |
Bazzan, Ana Lucia Cetertich |
author_facet |
Bazzan, Ana Lucia Cetertich Oliveira, Denise de Klugl, Franziska Nagel, Kai |
author_role |
author |
author2 |
Oliveira, Denise de Klugl, Franziska Nagel, Kai |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Bazzan, Ana Lucia Cetertich Oliveira, Denise de Klugl, Franziska Nagel, Kai |
dc.subject.por.fl_str_mv |
Inteligência artificial Engenharia : Trafego |
topic |
Inteligência artificial Engenharia : Trafego |
description |
One way to cope with the increasing traffic demand is to integrate standard solutions with more intelligent control measures. However, the result of possible interferences between intelligent control or information provision tools and other components of the overall traffic system is not easily predictable. This paper discusses the effects of integrating co-adaptive decision-making regarding route choices (by drivers) and control measures (by traffic lights). The motivation behind this is that optimization of traffic light control is starting to be integrated with navigation support for drivers. We use microscopic, agent-based modelling and simulation, in opposition to the classical network analysis, as this work focuses on the effect of local adaptation. In a scenario that exhibits features comparable to real-world networks, we evaluate different types of adaptation by drivers and by traffic lights, based on local perceptions. In order to compare the performance, we have also used a global level optimization method based on genetic algorithms. |
publishDate |
2008 |
dc.date.issued.fl_str_mv |
2008 |
dc.date.accessioned.fl_str_mv |
2011-08-16T06:01:15Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/bookPart |
format |
bookPart |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/31092 |
dc.identifier.nrb.pt_BR.fl_str_mv |
000683625 |
url |
http://hdl.handle.net/10183/31092 |
identifier_str_mv |
000683625 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
European Symposium on Adaptive and Learning Agents and Multi-Agent Systems (5. : 2005 : Paris, France) (6. : Brussel, Belgium) (7. : 2007 Apr. : Maastricht, The Netherlands). Revised Selected Papers. Berlin : Springer, 2008. p. 1-14 |
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openAccess |
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application/pdf |
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UFRGS |
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