A Differential Evolution Algorithm‐Based Traffic Control Model for Signalized Intersections

Çakıcı, Ziya; Murat, Yetiş Şazi

doi:10.1155/2019/7360939

Cited by 26 publications

(15 citation statements)

References 25 publications

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“…DE and AIS (artificial immune system) are some other heuristic search optimization techniques that have been recently used successfully for traffic engineering applications [65][66][67][68][69]. AIS is inspired by human biological immune system.…”

Section: Previous Studiesmentioning

confidence: 99%

“…Differential evolution (DE), like GA, is a population-based optimization evolutionary algorithm characterized by its simplicity, robustness, and fast convergence to the objective function. In recent years, DE has been successfully used for signalized intersection management [66,80]. DE is capable of solving non-differentiable and non-linear optimization problems more efficiently [81].…”

Section: Differential Evolution (De)mentioning

confidence: 99%

“…Table 3 presents optimization objective functions, control/decision variables, and constraints. The constraint limits were deemed desirable and recommended by a previous research study [66]. Figure 5 indicate the mean rating of the best candidate solutions in the population at each iteration for 30 executions set for both algorithms [82].…”

Section: Algorithm Procedures and Parmeters Settingmentioning

confidence: 99%

“…Table 4 presents optimized signal cycle lengths and corresponding green splits for each bound. Green splits were the input decision variables for both algorithms and were programmed and controlled to have values between 8 to 45 s, as recommended by a previous study [66]. The objective was to select the best possible combination of green splits in response approaching traffic volume to minimize the objective function i.e., average vehicle delay at the intersection.…”

Section: Algorithm Procedures and Parmeters Settingmentioning

confidence: 99%

See 3 more Smart Citations

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

et al. 2020

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Traffic signal control is an integral component of an intelligent transportation system (ITS) that play a vital role in alleviating traffic congestion. Poor traffic management and inefficient operations at signalized intersections cause numerous problems as excessive vehicle delays, increased fuel consumption, and vehicular emissions. Operational performance at signalized intersections could be significantly enhanced by optimizing phasing and signal timing plans using intelligent traffic control methods. Previous studies in this regard have mostly focused on lane-based homogenous traffic conditions. However, traffic patterns are usually non-linear and highly stochastic, particularly during rush hours, which limits the adoption of such methods. Hence, this study aims to develop metaheuristic-based methods for intelligent traffic control at isolated signalized intersections, in the city of Dhahran, Saudi Arabia. Genetic algorithm (GA) and differential evolution (DE) were employed to enhance the intersection’s level of service (LOS) by optimizing the signal timings plan. Average vehicle delay through the intersection was selected as the primary performance index and algorithms objective function. The study results indicated that both GA and DE produced a systematic signal timings plan and significantly reduced travel time delay ranging from 15 to 35% compared to existing conditions. Although DE converged much faster to the objective function, GA outperforms DE in terms of solution quality i.e., minimum vehicle delay. To validate the performance of proposed methods, cycle length-delay curves from GA and DE were compared with optimization outputs from TRANSYT 7F, a state-of-the-art traffic signal simulation, and optimization tool. Validation results demonstrated the adequacy and robustness of proposed methods.

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Section: Previous Studiesmentioning

confidence: 99%

Section: Differential Evolution (De)mentioning

confidence: 99%

Section: Algorithm Procedures and Parmeters Settingmentioning

confidence: 99%

Section: Algorithm Procedures and Parmeters Settingmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

et al. 2020

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“…Selecting the best parameters of a genetic algorithm, so as to obtain good results to optimize its performance, is very important to its effectiveness. Crossover, mutation rate and population size are the most influencing control parameters as reported by previous works [16][17][18][19][20]. However, pressure selection and population size in correlation is a new approach in balancing and GA algorithm optimization.…”

Section: Introductionmentioning

confidence: 92%

Optimizing Genetic Algorithm Performance for Effective Traffic Lights Control using Balancing Technique (GABT)

Iskandarani¹

2020

IJACSA

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Genetic Algorithm (GA) is implemented and simulation tested for the purpose of adaptable traffic lights management at four roads-intersection. The employed GA uses hybrid Boltzmann Selection (BS) and Roulette Wheel Selection techniques (BS-RWS). Selection Pressure (SP) and Population (Pop) parameters are used to tune and balance the designed GA to obtain optimized and correct control of passing vehicles. A very successful implementation of such parameters resulted in obtaining minimum number of Iterations (IRN) for a wide spectrum of SP and Pop. The algorithm is mathematically modeled and analyzed and a proof is obtained regarding the condition for balanced GA. Such Balanced GA is most useful in traffic management for an optimized Intelligent Transportation Systems, as it requires minimum iterations for convergence with faster dynamic controlling time.

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A hybrid traffic controller system based on flower pollination algorithm and type-2 fuzzy logic optimized with crow search algorithm for signalized intersections

Korkmaz,

Akgüngör

2024

Soft Comput

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In this study, a hybrid traffic signal control (HTSC) system based on phase and time optimization was developed. The Flower Pollination Algorithm (FPA) approach was used for phase optimization, while Type-2 Fuzzy Logic, optimized with the Crow Search Algorithm (CSA), was utilized for time optimization. The hybrid system's performance was investigated using nine different traffic conditions and four different intersection geometries. The hybrid system was compared with three controller systems which are a fixed-time signal controller, a signal controller based on the FPA approach (FPA_TSC), and the optimized Type-1 fuzzy logic signal controller (Type-1 FL-TSC). The HTSC approach achieved the best performance with about 32% improvement over the fixed-time traffic controller and it showed 5% and 6% better performance than the FPA_TSC and Type-1 FL-TSC, respectively. Considering the performance of the new hybrid system, it is effective in minimizing delays and driver dissatisfaction occurring from signalization. It also contributes to the reduction of emissions and fuel consumption. The HTSC approach can be used as an alternative signal control method in the control of intersections with high traffic volume due to its fast and effective performance.

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A Differential Evolution Algorithm‐Based Traffic Control Model for Signalized Intersections

Cited by 26 publications

References 25 publications

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

Optimizing Genetic Algorithm Performance for Effective Traffic Lights Control using Balancing Technique (GABT)

A hybrid traffic controller system based on flower pollination algorithm and type-2 fuzzy logic optimized with crow search algorithm for signalized intersections

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