Modeling and Simulation of Urban Transport Networks: Six Cases in Lima

Descripción del Articulo

Studies of urban transport networks in many cities are not well focused, since they do not include tools for their planning and control, making decisions to solve the multiple problems regarding traffic congestion expensive and not adequate, generating discomfort in the users, and many times it aggr...

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Detalles Bibliográficos
Autores: Córdova Serrano, Jesús A., Campos Briceño, Sheila P., Delgadillo, Rosa, Mauricio, David
Formato: artículo
Fecha de Publicación:2020
Institución:Universidad Nacional Mayor de San Marcos
Repositorio:Revistas - Universidad Nacional Mayor de San Marcos
Lenguaje:español
OAI Identifier:oai:ojs.csi.unmsm:article/19391
Enlace del recurso:https://revistasinvestigacion.unmsm.edu.pe/index.php/rpcsis/article/view/19391
Nivel de acceso:acceso abierto
Materia:Model
transport network
simulation
traffic congestion
Modelos
red de transporte
simulación
congestión vehicular
Descripción
Sumario:Studies of urban transport networks in many cities are not well focused, since they do not include tools for their planning and control, making decisions to solve the multiple problems regarding traffic congestion expensive and not adequate, generating discomfort in the users, and many times it aggravates the problems, since its impact is not evaluated. Currently exist a variety of simulators that are helpful for traffic simulation and online monitoring. An alternative to this are traffic simulators, which currently exist in variety, however, they are not easily accessible, a formal study and a relationship with the research center that provides it is required, their personalized reports are limited and they comply to another reality. In the present work, six frequent cases of urban transport networks in Lima are modeled, simulated and validated: intersection, oval, union, by-pass, clover and T; by using Arena (general-purpose simulator), known statistical and simulation techniques. The T model was validated with an average confidence level of 95%, in addition, personalized information could be obtained for decision making.
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