Influence of meta-heuristic optimization on the performance of adaptive interval type2-fuzzy traffic signal controllers

Araghi, Sahar; Khosravi, Abbas; Creighton, Douglas; Nahavandi, Saeid

doi:10.1016/j.eswa.2016.10.066

Cited by 24 publications

(10 citation statements)

References 34 publications

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“…The papers [22][23][24][25][26][27][28] are employed a type of artificial learning algorithm for solving the TST problem. Among these studies, Neural Networks, Adaptive Neuro-Fuzzy Inference System, Q-Learning, fuzzy logic, and Deep Reinforcement learning are the adapted machine learning algorithms.…”

Section: Artificial Intelligence-based Approachesmentioning

confidence: 99%

“…Among these studies, Neural Networks, Adaptive Neuro-Fuzzy Inference System, Q-Learning, fuzzy logic, and Deep Reinforcement learning are the adapted machine learning algorithms. Different objectives have been used in these studies including minimization of average delay [22,27], total travel time [24,25], average queue length [26], optimization of TST plan [23], and maximization of the flow rate [28].…”

Section: Artificial Intelligence-based Approachesmentioning

confidence: 99%

“…The signal control and optimization toolboxes were integrated into the software embedded in the controller's device. In [24] Araghi et al assessed the performance of three meta-heuristic algorithms, which were Simulated Annealing (SA), GA, and the CS on a complex advanced interval type-2 adaptive neuro-fuzzy inference system (IT2ANFIS) based traffic signal controller. Miletić et al [25] compared the effectiveness of two different approaches used in premature traffic light control systems through six different scenarios of microsimulation models using real-time data.…”

Section: Artificial Intelligence-based Approachesmentioning

confidence: 99%

See 2 more Smart Citations

State-of-art review of traffic signal control methods: challenges and opportunities

Qadri

Gökçe

Öner

2020

Eur. Transp. Res. Rev.

118

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Introduction Due to the menacing increase in the number of vehicles on a daily basis, abating road congestion is becoming a key challenge these years. To cope-up with the prevailing traffic scenarios and to meet the ever-increasing demand for traffic, the urban transportation system needs effective solution methodologies. Changes made in the urban infrastructure will take years, sometimes may not even be feasible. For this reason, traffic signal timing (TST) optimization is one of the fastest and most economical ways to curtail congestion at the intersections and improve traffic flow in the urban network. Purpose Researchers have been working on using a variety of approaches along with the exploitation of technology to improve TST. This article is intended to analyze the recent literature published between January 2015 and January 2020 for the computational intelligence (CI) based simulation approaches and CI-based approaches for optimizing TST and Traffic Signal Control (TSC) systems, provide insights, research gaps and possible directions for future work for researchers interested in the field. Methods In analyzing the complex dynamic behavior of traffic streams, simulation tools have a prominent place. Nowadays, microsimulation tools are frequently used in TST related researches. For this reason, a critical review of some of the widely used microsimulation packages is provided in this paper. Conclusion Our review also shows that approximately 77% of the papers included, utilizes a microsimulation tool in some form. Therefore, it seems useful to include a review, categorization, and comparison of the most commonly used microsimulation tools for future work. We conclude by providing insights into the future of research in these areas.

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Section: Artificial Intelligence-based Approachesmentioning

confidence: 99%

Section: Artificial Intelligence-based Approachesmentioning

confidence: 99%

Section: Artificial Intelligence-based Approachesmentioning

confidence: 99%

See 1 more Smart Citation

State-of-art review of traffic signal control methods: challenges and opportunities

Qadri

Gökçe

Öner

2020

Eur. Transp. Res. Rev.

118

View full text Add to dashboard Cite

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“…Traffic depends on road capacity, the amount of traffic that wants to move, but if the road capacity cannot accommodate, then the existing traffic will be hampered and will flow according to the maximum road network capacity [3]. Traffic congestion on the highway segment occurs when the flow of traffic vehicles increases with increasing travel demand in a certain period and the number of road users exceeds the existing capacity [4].…”

Section: A R T I C L E I N F Omentioning

confidence: 99%

Calculating vehicle intensiveness increase on eid al-fitr day with anfis method

Pratama¹,

Utami²,

Wibowo³

2018

IJARLIT

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the number of motorized vehicles increases every year, especially private vehicles and is not offset by inadequate access until the road becomes more crowded, even traffic jams occur, especially during public holidays and national holidays. for example, during eid holidays there is a density of traffic flow when going back and forth every year, with the development of current technology the density of traffic flows that occur can be calculated so that it will be easier to anticipate in the future. but in this study only will examine the parameter values that cause the vehicle to occur density and accumulation, because it can be developed with parameter values so that the results can be obtained efficiently in solving traffic density. From the results of the anfis method, efficiency is obtained, namely on h-1 and h days of 2014, and 2017 can use Parameters with magins of 6,3% and 4,32%, while 2015 and 2016 can use parameters with margins 1,79% and 0,79%. and for the h + 1 day of 2014, 2016, and 2017, it is more efficient to use parameters with margins 1,4% and only parameters in 2015 which have the efficiency value using parameters with margin -6,17. anfis application in this calculation can be developed in a prediction system.

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“…The proposed technique was then used in a research by the Zhang and Kucukkoc combines Artificial Intelligence and parallel computing [17]. Also research by Chou et al, Tosta et al, Araghi et al, all utilizing the combination of genetic algorithm and fuzzy logic [18][19][20]. As well as Li et al used hybrid algorithm which is achieved by combination of genetic algorithm and particle swarm optimization.…”

Section: Introductionmentioning

confidence: 99%

An Effective Improved Multi-objective Evolutionary Algorithm (IMOEA) for Solving Constraint Civil Engineering Optimization Problems

2021

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This paper introduces a new metaheuristic optimization method based on evolutionary algorithms to solve single-objective engineering optimization problems faster and more efficient. By considering constraints as a new objective function, problems turned to multi objective optimization problems. To avoid regular local optimum, different mutations and crossovers are studied and the best operators due their performances are selected as main operators of algorithm. Moreover, certain infeasible solutions can provide useful information about the direction which lead to best solution, so these infeasible solutions are defined on basic concepts of optimization and uses their feature to guide convergence of algorithm to global optimum. Dynamic interference of mutation and crossover are considered to prevent unnecessary calculation and also a selection strategy for choosing optimal solution is introduced. To verify the performance of the proposed algorithm, some CEC 2006 optimization problems which prevalently used in the literatures, are inspected. After satisfaction of acquired result by proposed algorithm on mathematical problems, four popular engineering optimization problems are solved. Comparison of results obtained by proposed algorithm with other optimization algorithms show that the suggested method has a powerful approach in finding the optimal solutions and exhibits significance accuracy and appropriate convergence in reaching the global optimum.

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Influence of meta-heuristic optimization on the performance of adaptive interval type2-fuzzy traffic signal controllers

Cited by 24 publications

References 34 publications

State-of-art review of traffic signal control methods: challenges and opportunities

State-of-art review of traffic signal control methods: challenges and opportunities

Calculating vehicle intensiveness increase on eid al-fitr day with anfis method

An Effective Improved Multi-objective Evolutionary Algorithm (IMOEA) for Solving Constraint Civil Engineering Optimization Problems

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