An agent-based approach for road pricing : system-level performance and implications for drivers
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/110285 |
Resumo: | Background: Road pricing is a useful mechanism to align private utility of drivers with a system-level measure of performance. Traffic simulation can be used to predict the impact of road pricing policies. The simulation is not a trivial task because traffic is a social system composed of different interacting entities. To tackle this complexity, agent-based approaches can be employed to model the behavior of the several actors in transportation systems. Methods: We model traffic as a multiagent system in which link manager agents employ a reinforcement learning scheme to determine road pricing policies in a road network. Drivers who traverse the road network are cost-minimizer agents with local information and different preferences regarding travel time and credits expenditure. Results: The vehicular flow achieved by our reinforcement learning approach for road pricing is close to a method where drivers have global information of the road network status to choose their routes. Our approach reaches its peak performance faster than a fixed pricing approach. Moreover, drivers’ welfare is greater when the variability of their preferences regarding minimization of travel time or credits expenditure is higher. Conclusions: Our experiments showed that the adoption of reinforcement learning for determining road pricing policies is a promising approach, even with limitations in the driver agent and link manager models. |
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Tavares, Anderson RochaBazzan, Ana Lucia Cetertich2015-02-21T01:57:04Z20140104-6500http://hdl.handle.net/10183/110285000946367Background: Road pricing is a useful mechanism to align private utility of drivers with a system-level measure of performance. Traffic simulation can be used to predict the impact of road pricing policies. The simulation is not a trivial task because traffic is a social system composed of different interacting entities. To tackle this complexity, agent-based approaches can be employed to model the behavior of the several actors in transportation systems. Methods: We model traffic as a multiagent system in which link manager agents employ a reinforcement learning scheme to determine road pricing policies in a road network. Drivers who traverse the road network are cost-minimizer agents with local information and different preferences regarding travel time and credits expenditure. Results: The vehicular flow achieved by our reinforcement learning approach for road pricing is close to a method where drivers have global information of the road network status to choose their routes. Our approach reaches its peak performance faster than a fixed pricing approach. Moreover, drivers’ welfare is greater when the variability of their preferences regarding minimization of travel time or credits expenditure is higher. Conclusions: Our experiments showed that the adoption of reinforcement learning for determining road pricing policies is a promising approach, even with limitations in the driver agent and link manager models.application/pdfengJournal of the Brazilian Computer Society. Rio de Janeiro. Vol. 20, no. 15 (2014), p. 1-15Sistemas multiagentesInformatica : TransportesRoad pricingMultiagent systemAgent-based simulationAn agent-based approach for road pricing : system-level performance and implications for driversinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/otherinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSORIGINAL000946367.pdf000946367.pdfTexto completo (inglês)application/pdf1638989http://www.lume.ufrgs.br/bitstream/10183/110285/1/000946367.pdf63455687e211aa6f5daa328317410052MD51TEXT000946367.pdf.txt000946367.pdf.txtExtracted Texttext/plain69969http://www.lume.ufrgs.br/bitstream/10183/110285/2/000946367.pdf.txtb16796e49d49823925cb8ba918be22aaMD52THUMBNAIL000946367.pdf.jpg000946367.pdf.jpgGenerated Thumbnailimage/jpeg2037http://www.lume.ufrgs.br/bitstream/10183/110285/3/000946367.pdf.jpg2bd6415932abce7213206b938856f110MD5310183/1102852018-10-23 09:21:53.748oai:www.lume.ufrgs.br:10183/110285Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2018-10-23T12:21:53Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
An agent-based approach for road pricing : system-level performance and implications for drivers |
title |
An agent-based approach for road pricing : system-level performance and implications for drivers |
spellingShingle |
An agent-based approach for road pricing : system-level performance and implications for drivers Tavares, Anderson Rocha Sistemas multiagentes Informatica : Transportes Road pricing Multiagent system Agent-based simulation |
title_short |
An agent-based approach for road pricing : system-level performance and implications for drivers |
title_full |
An agent-based approach for road pricing : system-level performance and implications for drivers |
title_fullStr |
An agent-based approach for road pricing : system-level performance and implications for drivers |
title_full_unstemmed |
An agent-based approach for road pricing : system-level performance and implications for drivers |
title_sort |
An agent-based approach for road pricing : system-level performance and implications for drivers |
author |
Tavares, Anderson Rocha |
author_facet |
Tavares, Anderson Rocha Bazzan, Ana Lucia Cetertich |
author_role |
author |
author2 |
Bazzan, Ana Lucia Cetertich |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Tavares, Anderson Rocha Bazzan, Ana Lucia Cetertich |
dc.subject.por.fl_str_mv |
Sistemas multiagentes Informatica : Transportes |
topic |
Sistemas multiagentes Informatica : Transportes Road pricing Multiagent system Agent-based simulation |
dc.subject.eng.fl_str_mv |
Road pricing Multiagent system Agent-based simulation |
description |
Background: Road pricing is a useful mechanism to align private utility of drivers with a system-level measure of performance. Traffic simulation can be used to predict the impact of road pricing policies. The simulation is not a trivial task because traffic is a social system composed of different interacting entities. To tackle this complexity, agent-based approaches can be employed to model the behavior of the several actors in transportation systems. Methods: We model traffic as a multiagent system in which link manager agents employ a reinforcement learning scheme to determine road pricing policies in a road network. Drivers who traverse the road network are cost-minimizer agents with local information and different preferences regarding travel time and credits expenditure. Results: The vehicular flow achieved by our reinforcement learning approach for road pricing is close to a method where drivers have global information of the road network status to choose their routes. Our approach reaches its peak performance faster than a fixed pricing approach. Moreover, drivers’ welfare is greater when the variability of their preferences regarding minimization of travel time or credits expenditure is higher. Conclusions: Our experiments showed that the adoption of reinforcement learning for determining road pricing policies is a promising approach, even with limitations in the driver agent and link manager models. |
publishDate |
2014 |
dc.date.issued.fl_str_mv |
2014 |
dc.date.accessioned.fl_str_mv |
2015-02-21T01:57:04Z |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/other |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
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http://hdl.handle.net/10183/110285 |
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0104-6500 |
dc.identifier.nrb.pt_BR.fl_str_mv |
000946367 |
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http://hdl.handle.net/10183/110285 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Journal of the Brazilian Computer Society. Rio de Janeiro. Vol. 20, no. 15 (2014), p. 1-15 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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