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Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.18 n.1 México Jan./Mar. 2014

http://dx.doi.org/10.13053/CyS-18-1-2014-017 

Artículos

 

Traffic Flow Estimation Using Ant Colony Optimization Algorithms

 

Algoritmos de optimización basados en colonias de hormigas para la estimación de flujos de tráfico

 

Antonio Bolufé-Röhler1, Juan Manuel Otero Pereira1, and Sonia Fiol-González1

 

1 Facultad de Matemática y Computación, Universidad de la Habana, Cuba. bolufe@matcom.uh.cu, otero@matcom.uh.cu, s.fiol@matcom.uh.cu

 

Abstract

Simulation and optimization of traffic flows in a city or province allow the implementation of correct developing strategies and help the decision making process when using and distributing resources such as mass transit. This estimation can be modeled as a bifurcated multi-commodity network flow problem, where the general flow distribution is dictated by Wardrop's principles. In this paper two different Ant Colony Optimization algorithms are presented for solving this problem. The proposed algorithms are tested with real-life traffic demand in the Havana city. The obtained results are compared to those provided by classical algorithms, showing that the new ant colony algorithms provide good results as well as low running times.

Keywords: Non-linear optimization, metaheuristics, traffic problem, logistics, simulation.

 

Resumen

La estimación de flujos de tráfico permite implementar buenas estrategias de desarrollo, a la vez que ayuda en el proceso de toma de decisiones cuando se controlan y distribuyen recursos claves como el transporte masivo. La distribución de tráfico puede ser modelada como un problema de Flujo de Costo Mínimo para Múltiples Bienes. Para su solución, la Optimización de Colonia de Hormigas provee un marco de trabajo prometedor. En la presente investigación se presentan dos nuevos algoritmos basados en Colonias de Hormigas, los mismos se aplican a instancias reales del problema de estimación de flujo en Ciudad de La Habana. Los resultados alcanzados se comparan con los provistos por algoritmos clásicos, mostrando la efectividad del método propuesto.

Palabras clave: Optimización no-lineal, metaheurísticas, problema de tráfico, logística, simulación.

 

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