Ant Colony Optimization Approach for Optimizing Traffic Signal Timings

Başkan, Özgür; Haldenbilen, Soner

doi:10.5772/13665

Cited by 18 publications

(9 citation statements)

References 18 publications

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“…Otra investigación que utiliza un GA es el trabajo desarrollado por Dezani et al (2012) que utiliza el algoritmo para resolver este problema aplicándolo a una red de tráfico urbano congestionado en un escenario en tiempo real con resultados prometedores. Pasando a otra metaheurística, Baskan y Haldenbilen (2008) y Baskan et al (2011) implementan una variante de Optimización de Colonia de Hormigas (ACO) donde su enfoque se utiliza para el espacio de búsqueda de la solución potencial de un problema de optimización de tráfico de dos niveles. Jabbarpour et al (2014) ofrecen una descripción general completa de los sistemas de control de congestión y enrutamiento de tráfico de vehículos basados en Colonia de Hormigas.…”

Section: Revision De La Literaturaunclassified

Funciones de evaluación de calidad para un tránsito inteligente

Carballo¹,

Villagra²,

Pandolfi³

2021

ICTUNPA

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Los problemas de tránsito aumentan constantemente y las ciudades del futuro solo pueden ser verdaderamente inteligentes si habilitan la Movilidad Inteligente (en inglés, Smart Mobility). Aplicaciones de movilidad inteligente como el control de semáforos, el estacionamiento inteligente y la gestión del tránsito, entre otras están comenzando a ser utilizadas en todo el mundo, trayendo beneficios a las ciudades, mejor calidad de vida, costos reducidos, uso de energía más eficiente y disminución de las emisiones vehiculares. Mejorar la movilidad representa un gran reto. Actualmente, una ciudad con tráfico congestionado y muchos embotellamientos conduce a más contaminación. Por lo tanto, con la gestión inteligente del tránsito, el consumo de combustible y las emisiones contaminantes pueden ser reducidas. A diferencia de técnicas tales como semáforos inteligentes que requieren nueva infraestructura, ubicación de sensores y modificaciones en obras civiles ya construidas, el uso de técnicas de inteligencia artificial en la optimización de los ciclos de semáforos se presenta como una herramienta viable, rápida, eficiente y de bajo costo. En este trabajo se proponen diferentes funciones de evaluación de calidad con un algoritmo genético celular (cGA) aplicadas a dos grandes escenarios cercanos a la realidad en áreas urbanas ubicadas en las ciudades de Málaga (España) y París (Francia), simuladas por el popular micro-simulador SUMO (Simulator Urban MObility). La comparación de las diferentes funciones revela la bondad de algunas funciones logrando a una reducción significativa en términos de las tasas de emisión y del consumo total de combustible, además de una reducción en los tiempos de variables de tránsito analizadas.

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Section: Revision De La Literaturaunclassified

Funciones de evaluación de calidad para un tránsito inteligente

Carballo¹,

Villagra²,

Pandolfi³

2021

ICTUNPA

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“…The core of ant's behavior is the communication between the ants by means of chemical pheromone trails, which enables them to find shortest paths between their nest and food sources. This behaviour of real ant colonies is exploited to solve optimization problems [20]. The general ACO algorithm is illustrated in Fig.…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

An Ant Colony Optimization Algorithm for Area Traffic Control

Haldenbilen¹,

Başkan²,

Ozan³

2013

Ant Colony Optimization - Techniques and Applications

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runs, using the TRANSYT-7F simulation engine. Each simulation run is assigned a unique signal timing plan by the optimization processor. The optimizer applies the Hill-Climbing (HC) or Genetic Algorithm (GA) searching strategies. The trial simulation run resulting in the best performance is reported as optimal. Although the GA is mathematically better suited for determining the absolute or global optimal solution, relative to HC optimization, it generally requires longer program running times, relative to HC optimization [4]. This chapter proposes Ant Colony Optimization (ACO) based algorithm called ACORSES proposed by [5] for finding optimum signal parameters in coordinated signalized networks for given fixed set of link flows. The ACO is the one of the most recent techniques for approximate optimization methods. The main idea is that it is indirect local communication among the individuals of a population of artificial ants. The core of ant's behaviour is the communication between the ants by means of chemical pheromone trails, which enables them to find shortest paths between their nest and food sources. This behaviour of real ant colonies is exploited to solve optimization problems. The proposed algorithm is based on each ant searches only around the best solution of the previous iteration with reduced search space. It is proposed for improving ACO's solution performance to reach global optimum fairly quickly. In this study, for solving the ATC problem, Ant Colony Optimization TRANSYT (ACOTRANS) model is developed. TRANSYT-7F traffic model is used to estimate total network PI.

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“…Koh [6] used differential evaluation for bilevel continuous network design problem. Ozgur Basken and Sonar Haldenbilen [7] developed an ACO Reduced Search Space (ACORSS) to find better signal timing in the signal setting problem of a bilevel model for traffic optimization problem. Hu Hui [8] solved urban transportation equilibrium network design problem using bilevel programming, and it was solved using PSO and Frank Wolfe.…”

Section: Introductionmentioning

confidence: 99%

Application of Bat Algorithm for Transport Network Design Problem

Srivastava

Sahana

2019

Applied Computational Intelligence and Soft Computing

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The requirement of the road services and transportation network development planning came into existence with the development of civilization. In the modern urban transport scenario with the forever mounting amount of vehicles, it is very much essential to tackle network congestion and to minimize the travel time. This work is based on determining the optimal wait time at traffic signals for the microscopic discrete model. The problem is formulated as a bilevel model. The upper layer optimizes the travel time by reducing the wait time at traffic signal and the lower layer solves the stochastic user equilibrium. Soft computing techniques like Genetic Algorithms, Ant Colony Optimization, and many other biologically inspired techniques prove to give good results for bilevel problems. Here this work uses Bat Intelligence to solve the transport network design problem. The results are compared with the existing techniques.

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Ant Colony Optimization Approach for Optimizing Traffic Signal Timings

Cited by 18 publications

References 18 publications

Funciones de evaluación de calidad para un tránsito inteligente

Funciones de evaluación de calidad para un tránsito inteligente

An Ant Colony Optimization Algorithm for Area Traffic Control

Application of Bat Algorithm for Transport Network Design Problem

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