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Título : Calibration of DTA model for a BRT system using computational intelligence techniques [recurso electrónico]
Autores: Jenniffer Sidney, Guerrero Prado
Caicedo Bravo, Eduardo Francisco (Director de Tesis o Trabajo de Grado)
Morales, Wilfredo Alfonso (Director de Tesis o Trabajo de Grado)
Palabras clave : Ingeniería del transporte
Inteligencia computacional
Procesos de optimización
Calibración de modelos computacionales
Fecha de publicación: 29-nov-2019
Resumen: Modeling of a public transport system is the first step properly know how a system transport behaves itself and how is the interaction with users, driver behaviors, roads, traffic signs and private vehicles. The first attempts to develop traffic models can be dated to the 1930s (Adams, 1936; Greenshields, Bibbins, Channing, & Miller, 1935) but even today there are not satisfactory and general mathematical theory to describe real traffic flow conditions. This is because traffic systems are complex and nonlinear, and depend on the interactions of a large number of vehicles, among others. Modelling these systems means, consequently, an effort to find ways to enhance the level of service, especially using technology, such as applying high performance computing and information technology to transportation systems, which can only be achieved with sophisticated simulation modeling and computational intelligence tools. Using a simulated model to represent a public transport system could support transportation planning, traffic management, decision making, for a long term sustainable urban development. (Papageorgiou et al., 2009). When all factors involved in public transportation systems are taken into consideration, such as those mentioned above in BRT systems, and include private transport, traffic light systems, people or users, etc., with mobility needs, an Urban Traffic System is obtained.To model any system of urban traffic, it is necessary to know the available approaches for this purpose. So far, the literature provides two main approaches to modeling, static or dynamic, which depend on how the interactions between supply and demand are contemplated. The dynamic modeling, in turn, can be classified in microscopic, mesoscopic or macroscopic scope and depends on the level of detail desired in the simulation. These models may correspond to systems of private or public transport assignment, which are known as Dynamic Traffic Assignment (DTA1) or Dynamic Transit Assignment (DTA2) respectively.
URI: http://hdl.handle.net/10893/14678
Aparece en las colecciones: Maestría en Ingeniería - Énfasis en Ingeniería Eléctrónica

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