Modeling vehicular traffic networks. Part I

Detalhes bibliográficos
Autor(a) principal: Otero, Dino
Data de Publicação: 2018
Outros Autores: Galetti, Diógenes [UNESP], Mizrahi, Salomon S.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.physa.2018.06.016
http://hdl.handle.net/11449/176457
Resumo: We propose three models for the traffic of vehicles within a network formed by sites (cities, car-rental agencies, parking lots, etc.) and connected by two-way arteries (roads, highways), that allow forecasting the vehicular flux in a sequence of n consecutive steps, or units of time. An essential approach consists in using, as an a priori information, previous observations and measurements. The formal tools used in our analysis consists in: (1) associating a digraph to the network where the edges correspond to arteries and the vertices with loops represent the sites. (2) From a distribution of vehicles within the network, we construct a matrix from which we derive a stochastic matrix (SM). This matrix becomes the generator of the evolution of the traffic flow. And (3), we use the Perron–Frobenius theory for a formal analysis. We investigate three models: (a) a closed network with conserved number of vehicles; (b) to this network we add an influx and an outflux of vehicles to picture an open system. And (c), we construct a nonlinear model whose formal structure exhibits the existence of several (L) stationary states for the distribution of vehicles at each site, that alternate cyclically with time. Each state represents the traffic for L different moments. These models are hybridized and compared numerically to the effective vehicular traffic in a sector of the city of Tigre, localized in the province of Buenos Aires, Argentina. The empirical data and the traffic modelization are presented in a following paper, referred as Part II.
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spelling Modeling vehicular traffic networks. Part IDigraph theoryLinear and nonlinear modelsMarkov chainNetworkPerron–Frobenius theoryStochastic matrixVehicular trafficWe propose three models for the traffic of vehicles within a network formed by sites (cities, car-rental agencies, parking lots, etc.) and connected by two-way arteries (roads, highways), that allow forecasting the vehicular flux in a sequence of n consecutive steps, or units of time. An essential approach consists in using, as an a priori information, previous observations and measurements. The formal tools used in our analysis consists in: (1) associating a digraph to the network where the edges correspond to arteries and the vertices with loops represent the sites. (2) From a distribution of vehicles within the network, we construct a matrix from which we derive a stochastic matrix (SM). This matrix becomes the generator of the evolution of the traffic flow. And (3), we use the Perron–Frobenius theory for a formal analysis. We investigate three models: (a) a closed network with conserved number of vehicles; (b) to this network we add an influx and an outflux of vehicles to picture an open system. And (c), we construct a nonlinear model whose formal structure exhibits the existence of several (L) stationary states for the distribution of vehicles at each site, that alternate cyclically with time. Each state represents the traffic for L different moments. These models are hybridized and compared numerically to the effective vehicular traffic in a sector of the city of Tigre, localized in the province of Buenos Aires, Argentina. The empirical data and the traffic modelization are presented in a following paper, referred as Part II.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Facultad Regional General Pacheco Universidad Técnica Nacional (UTN)Instituto de Física Teórica Universidade Estadual Paulista (UNESP)Departamento de Física CCET Universidade Federal de São Carlos (UFSCar)Instituto de Física Teórica Universidade Estadual Paulista (UNESP)Universidad Técnica Nacional (UTN)Universidade Estadual Paulista (Unesp)Universidade Federal de São Carlos (UFSCar)Otero, DinoGaletti, Diógenes [UNESP]Mizrahi, Salomon S.2018-12-11T17:20:52Z2018-12-11T17:20:52Z2018-11-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article97-110application/pdfhttp://dx.doi.org/10.1016/j.physa.2018.06.016Physica A: Statistical Mechanics and its Applications, v. 509, p. 97-110.0378-4371http://hdl.handle.net/11449/17645710.1016/j.physa.2018.06.0162-s2.0-850485626372-s2.0-85048562637.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPhysica A: Statistical Mechanics and its Applications0,773info:eu-repo/semantics/openAccess2024-01-05T06:22:30Zoai:repositorio.unesp.br:11449/176457Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T22:09:56.039758Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Modeling vehicular traffic networks. Part I
title Modeling vehicular traffic networks. Part I
spellingShingle Modeling vehicular traffic networks. Part I
Otero, Dino
Digraph theory
Linear and nonlinear models
Markov chain
Network
Perron–Frobenius theory
Stochastic matrix
Vehicular traffic
title_short Modeling vehicular traffic networks. Part I
title_full Modeling vehicular traffic networks. Part I
title_fullStr Modeling vehicular traffic networks. Part I
title_full_unstemmed Modeling vehicular traffic networks. Part I
title_sort Modeling vehicular traffic networks. Part I
author Otero, Dino
author_facet Otero, Dino
Galetti, Diógenes [UNESP]
Mizrahi, Salomon S.
author_role author
author2 Galetti, Diógenes [UNESP]
Mizrahi, Salomon S.
author2_role author
author
dc.contributor.none.fl_str_mv Universidad Técnica Nacional (UTN)
Universidade Estadual Paulista (Unesp)
Universidade Federal de São Carlos (UFSCar)
dc.contributor.author.fl_str_mv Otero, Dino
Galetti, Diógenes [UNESP]
Mizrahi, Salomon S.
dc.subject.por.fl_str_mv Digraph theory
Linear and nonlinear models
Markov chain
Network
Perron–Frobenius theory
Stochastic matrix
Vehicular traffic
topic Digraph theory
Linear and nonlinear models
Markov chain
Network
Perron–Frobenius theory
Stochastic matrix
Vehicular traffic
description We propose three models for the traffic of vehicles within a network formed by sites (cities, car-rental agencies, parking lots, etc.) and connected by two-way arteries (roads, highways), that allow forecasting the vehicular flux in a sequence of n consecutive steps, or units of time. An essential approach consists in using, as an a priori information, previous observations and measurements. The formal tools used in our analysis consists in: (1) associating a digraph to the network where the edges correspond to arteries and the vertices with loops represent the sites. (2) From a distribution of vehicles within the network, we construct a matrix from which we derive a stochastic matrix (SM). This matrix becomes the generator of the evolution of the traffic flow. And (3), we use the Perron–Frobenius theory for a formal analysis. We investigate three models: (a) a closed network with conserved number of vehicles; (b) to this network we add an influx and an outflux of vehicles to picture an open system. And (c), we construct a nonlinear model whose formal structure exhibits the existence of several (L) stationary states for the distribution of vehicles at each site, that alternate cyclically with time. Each state represents the traffic for L different moments. These models are hybridized and compared numerically to the effective vehicular traffic in a sector of the city of Tigre, localized in the province of Buenos Aires, Argentina. The empirical data and the traffic modelization are presented in a following paper, referred as Part II.
publishDate 2018
dc.date.none.fl_str_mv 2018-12-11T17:20:52Z
2018-12-11T17:20:52Z
2018-11-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1016/j.physa.2018.06.016
Physica A: Statistical Mechanics and its Applications, v. 509, p. 97-110.
0378-4371
http://hdl.handle.net/11449/176457
10.1016/j.physa.2018.06.016
2-s2.0-85048562637
2-s2.0-85048562637.pdf
url http://dx.doi.org/10.1016/j.physa.2018.06.016
http://hdl.handle.net/11449/176457
identifier_str_mv Physica A: Statistical Mechanics and its Applications, v. 509, p. 97-110.
0378-4371
10.1016/j.physa.2018.06.016
2-s2.0-85048562637
2-s2.0-85048562637.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Physica A: Statistical Mechanics and its Applications
0,773
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 97-110
application/pdf
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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