Artificial Bee Colony-Based Algorithm for Optimising Traffic Signal Timings

Dell’Orco, Mauro; Başkan, Özgür; Marinelli, Mario

doi:10.1007/978-3-319-00930-8_29

Cited by 17 publications

(12 citation statements)

References 17 publications

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“…Liu et al (2015) investigated an optimal variable speed limit (VSL) system capable of optimizing the system designs when the variable message signs (VMSs) are movable using a GA approach. Dell'Orco et al (2014) proposed an artificial bee colony algorithm to find the optimal settings of a traffic light controller in a simulation environment enabled by TRANSYT-7F which integrates a macroscopic simulation model.…”

Section: Literature Reviewmentioning

confidence: 99%

A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation

Jin

Kosonen

2017

Advances in Engineering Software

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A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation. Advances in Engineering SoftwareAccess to the published version may require subscription. N.B. When citing this work, cite the original published paper. AbstractTraffic flow is considered as a stochastic process in road traffic modeling. Computer simulation is a widely used tool to represent traffic system in engineering applications. The increased traffic congestion in urban areas and their impacts require more efficient controls and management. While the effectiveness of control schemes highly depends on accurate traffic model and appropriate control settings, optimization techniques play a central role for determining the control parameters in traffic planning and management applications. However, there is still a lack of research effort on the scientific computing framework for optimizing traffic control and operations and facilitating real planning and management applications. To this end, the present study proposes a model-based optimization framework to integrate essential components for solving road traffic control problems in general. In particular, the framework is based on traffic simulation models, while the solution needs extensive computation during the engineering optimization process. In this study, an advanced genetic algorithm, extended by an external archive for storing globally elite genes, governs the computing framework, and it shows superior performance than the ordinary genetic algorithm because of the reduced number of fitness function evaluations in engineering applications. To evaluate the optimization algorithm and validate the whole software framework, this paper illustrates a detailed application for optimization of traffic light controls. The study optimizes a simple road network of two intersections in Stockholm to demonstrate the model-based optimization processes as well as to evaluate the presented algorithm and software performance.

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Section: Literature Reviewmentioning

confidence: 99%

A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation

Jin

Kosonen

2017

Advances in Engineering Software

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“…Dell'Orco et al [42] presented a HS-based method using BLPM for signalized road networks by considering SUE link flows. Similarly, Dell'Orco et al [43] attempted to solve area traffic control problem by using Artificial Bee Colony (ABC) algorithm with TRANSYT-7F. Results revealed that the ABC algorithm outperformed the GA and HC methods in terms of PI.…”

Section: Introductionmentioning

confidence: 99%

A Simultaneous Solution for Reserve Capacity Maximization and Delay Minimization Problems in Signalized Road Networks

Başkan

Ceylan

Ozan

2019

Journal of Advanced Transportation

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In this study, we present a bilevel programming model in which upper level is defined as a biobjective problem and the lower level is considered as a stochastic user equilibrium assignment problem. It is clear that the biobjective problem has two objectives: the first maximizes the reserve capacity whereas the second minimizes performance index of a road network. We use a weighted-sum method to determine the Pareto optimal solutions of the biobjective problem by applying normalization approach for making the objective functions dimensionless. Following, a differential evolution based heuristic solution algorithm is introduced to overcome the problem presented by use of biobjective bilevel programming model. The first numerical test is conducted on two-junction network in order to represent the effect of the weighting on the solution of combined reserve capacity maximization and delay minimization problem. Allsop & Charlesworth’s network, which is a widely preferred road network in the literature, is selected for the second numerical application in order to present the applicability of the proposed model on a medium-sized signalized road network. Results support authorities who should usually make a choice between two conflicting issues, namely, reserve capacity maximization and delay minimization.

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“…Al-Kubaisi [11] bir regresyon modeli ile gecikmeyi minimum, devre süresini ise optimum yapmayı amaçlayan bir model önermiş ve önerdiği modeli doğrulamak amacıyla OSCADY/3 yazılımını kullanmıştır. Dell'Orco ve diğerleri [12,13]…”

Section: Introductionunclassified

Meta-Sezgisel Yöntemlerle Sabit Zamanlı Sinyalize Kavşaklar için Optimum Devre Süresi Modeli

Akgüngör¹,

Yılmaz²,

Korkmaz³

et al. 2019

El-Cezeri Fen Ve Mühendislik Dergisi

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Son zamanlarda nüfus ve ekonomideki büyüme karayollarında araç kullanımını arttırmaktadır. Buna bağlı olarak kavşakların kapasitesi giderek yetersiz kalmaktadır. Kavşakların verimsiz çalışmasından dolayı gecikme, yakıt tüketimi, emisyon salınımı artarken sürücü davranışları da olumsuz etkilenmektedir. Optimum devre süresinin doğru tespiti ve sinyal sürelerinin düzenlenmesi ile bu sorunların minimuma indirilebilmesi mümkün olmaktadır. Bu çalışmada Yapay Arı Kolonisi Algoritması (YAKA) kullanılarak optimum devre süresi modelleri geliştirilmiştir. Ayrıca en düşük gecikmeye sahip olan devre sürelerinin belirlenmesinde Diferansiyel Gelişim Algoritmasından (DGA) yararlanılmıştır. Kalibre edilen Webster modeline ilave olarak üstel ve kuadratik formda modeller geliştirilmiştir. Geliştirilen bütün modeller Webster modelinden istatistiksel olarak daha iyi performansa sahip olurken, en iyi performansı da üstel model vermiştir. Bu modellerin özellikle yüksek trafik hacmine sahip trafik durumlarında yetersiz kalan Webster modelinin eksikliğini kapatarak alternatif bir devre süresi tahmin modeli olarak kullanılabileceği görülmüştür.

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Artificial Bee Colony-Based Algorithm for Optimising Traffic Signal Timings

Cited by 17 publications

References 17 publications

A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation

A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation

A Simultaneous Solution for Reserve Capacity Maximization and Delay Minimization Problems in Signalized Road Networks

Meta-Sezgisel Yöntemlerle Sabit Zamanlı Sinyalize Kavşaklar için Optimum Devre Süresi Modeli

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