Urban traffic routing using weighted multi-map strategies

Urban traffic routing has to deal with individual mobility and collective wellness considering citizens, multi-modal transport, and fleet traffic with conflicting interests such as electric vehicles, local distribution, public transport, and private vehicles. Different interests, goals, and regulati...

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Detalles Bibliográficos
Autores: Paricio García, Álvaro|||0000-0002-9162-4147, López Carmona, Miguel Ángel|||0000-0001-9228-1863
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/60595
Acceso en línea:http://hdl.handle.net/10017/60595
https://dx.doi.org/10.1109/ACCESS.2019.2947699
Access Level:acceso abierto
Palabra clave:Dynamic traffic assignment
Traffic control
Vehicle routing
Traffic big data
Decision making
Multi-agent systems
Multi-map routing
TWM
Traffic simulationen
Informática
Computer science
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spelling Urban traffic routing using weighted multi-map strategiesParicio García, Álvaro|||0000-0002-9162-4147López Carmona, Miguel Ángel|||0000-0001-9228-1863Dynamic traffic assignmentTraffic controlVehicle routingTraffic big dataDecision makingMulti-agent systemsMulti-map routingTWMTraffic simulationenInformáticaComputer scienceUrban traffic routing has to deal with individual mobility and collective wellness considering citizens, multi-modal transport, and fleet traffic with conflicting interests such as electric vehicles, local distribution, public transport, and private vehicles. Different interests, goals, and regulations, suggest the development of new multi-objective routing mechanisms which may improve traffic flow. In this work, Traffic Weighted Multi-Maps (TWM) is presented as a novel traffic routing mechanism based on the strategical generation and distribution of complementary cost maps for traffic fleets, oriented towards the application of differentiated traffic planning and control policies. TWM is built upon a centralized control architecture, where a Traffic Management Center generates and distributes customized cost maps of the road network. These maps are used individually to calculate routes. In this research, we present the TWM theoretical model and experimental results based on microscopic simulations over a real city traffic network under multiple scenarios, including traffic incidents management. Experimental evaluation takes into account driver?s adherence to the system and considers a multi-objective analysis both for the global network parameters (congestion, travel time, and route length) and for the subjective driving experience. Experimental results deliver performance improvements from 20% to 50%. TWM is fully compatible with existing traffic routing systems and has promising future evolution applying new algorithms, policies and network profiles.IEEE20192019-10-31journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/60595https://dx.doi.org/10.1109/ACCESS.2019.2947699reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/605952026-06-18T11:13:07Z
dc.title.none.fl_str_mv Urban traffic routing using weighted multi-map strategies
title Urban traffic routing using weighted multi-map strategies
spellingShingle Urban traffic routing using weighted multi-map strategies
Paricio García, Álvaro|||0000-0002-9162-4147
Dynamic traffic assignment
Traffic control
Vehicle routing
Traffic big data
Decision making
Multi-agent systems
Multi-map routing
TWM
Traffic simulationen
Informática
Computer science
title_short Urban traffic routing using weighted multi-map strategies
title_full Urban traffic routing using weighted multi-map strategies
title_fullStr Urban traffic routing using weighted multi-map strategies
title_full_unstemmed Urban traffic routing using weighted multi-map strategies
title_sort Urban traffic routing using weighted multi-map strategies
dc.creator.none.fl_str_mv Paricio García, Álvaro|||0000-0002-9162-4147
López Carmona, Miguel Ángel|||0000-0001-9228-1863
author Paricio García, Álvaro|||0000-0002-9162-4147
author_facet Paricio García, Álvaro|||0000-0002-9162-4147
López Carmona, Miguel Ángel|||0000-0001-9228-1863
author_role author
author2 López Carmona, Miguel Ángel|||0000-0001-9228-1863
author2_role author
dc.subject.none.fl_str_mv Dynamic traffic assignment
Traffic control
Vehicle routing
Traffic big data
Decision making
Multi-agent systems
Multi-map routing
TWM
Traffic simulationen
Informática
Computer science
topic Dynamic traffic assignment
Traffic control
Vehicle routing
Traffic big data
Decision making
Multi-agent systems
Multi-map routing
TWM
Traffic simulationen
Informática
Computer science
description Urban traffic routing has to deal with individual mobility and collective wellness considering citizens, multi-modal transport, and fleet traffic with conflicting interests such as electric vehicles, local distribution, public transport, and private vehicles. Different interests, goals, and regulations, suggest the development of new multi-objective routing mechanisms which may improve traffic flow. In this work, Traffic Weighted Multi-Maps (TWM) is presented as a novel traffic routing mechanism based on the strategical generation and distribution of complementary cost maps for traffic fleets, oriented towards the application of differentiated traffic planning and control policies. TWM is built upon a centralized control architecture, where a Traffic Management Center generates and distributes customized cost maps of the road network. These maps are used individually to calculate routes. In this research, we present the TWM theoretical model and experimental results based on microscopic simulations over a real city traffic network under multiple scenarios, including traffic incidents management. Experimental evaluation takes into account driver?s adherence to the system and considers a multi-objective analysis both for the global network parameters (congestion, travel time, and route length) and for the subjective driving experience. Experimental results deliver performance improvements from 20% to 50%. TWM is fully compatible with existing traffic routing systems and has promising future evolution applying new algorithms, policies and network profiles.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-10-31
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10017/60595
https://dx.doi.org/10.1109/ACCESS.2019.2947699
url http://hdl.handle.net/10017/60595
https://dx.doi.org/10.1109/ACCESS.2019.2947699
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:e_Buah Biblioteca Digital Universidad de Alcalá
instname:Universidad de Alcalá (UAH)
instname_str Universidad de Alcalá (UAH)
reponame_str e_Buah Biblioteca Digital Universidad de Alcalá
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