To adapt or not to adapt : consequences of adapting driver and traffic light agents

Detalhes bibliográficos
Autor(a) principal: Bazzan, Ana Lucia Cetertich
Data de Publicação: 2008
Outros Autores: Oliveira, Denise de, Klugl, Franziska, Nagel, Kai
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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spelling 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.
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