Optimizing a fuzzy logic traffic signal controller via the differential evolution algorithm under different traffic scenarios

Doğan, Erdem; Akgüngör, Ali Payıdar

doi:10.1177/0037549716673217

Cited by 14 publications

(6 citation statements)

References 38 publications

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“…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%

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: Differential Evolution (De)mentioning

confidence: 99%

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

et al. 2020

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“…In addition to this, the degree of saturation for each lane was constrained with a maximum of 1.2. Differential Evolution (DE) algorithm which is a powerful and simple meta-heuristic optimization algorithm is preferred to solve the signal timing optimization problem [45][46][47][48][49]. A signal timing optimization program was developed in MATLAB for this purpose.…”

Section: Discussionmentioning

confidence: 99%

Design of an Efficient Vehicle-Actuated Signal Control Logic for Signalized Intersections

2021

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The effectiveness of fixed-time management systems dramatically decreases in case of fluctuated traffic demands at signalized intersection approaches. This leads to the waste of time in traffic and may cause material, psychological and ecological problems. Especially in recent years, to minimize the negative impacts of these problems, many researchers focus on Intelligent Transportation Systems (ITS). As it is known, one of the Intelligent Transportation Systems applications is called vehicle-actuated (traffic-actuated) management systems.Because the success of these types of applications is directly related to the created control logic, the selection of the most proper control parameters for the created control logic is an important issue. This study aims to create an effective control logic and flow chart for the vehicle-actuated management system. At the end of the analyses, it is seen that the created vehicle-actuated management system can adapt to fluctuations in traffic demands at signalized intersection approaches. Average vehicle delays, fuel consumptions and exhaust emissions can be reduced significantly by the created system. Especially, in case of fluctuations in traffic demands at 2 intersection approaches exist, it is concluded that the performance of the intersection can increase enormously with the created vehicle-actuated management system.

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“…DE is characterized by its robustness, fast convergence to the objective function, and simplicity. Though the method has been successfully used for numerous applications across different disciplines, only a few studies have adopted DE for urban traffic control and management [25][26][27][28][29]. For example, in their recent study, Jamal et al compared the performance of GA and DE for optimizing traffic lights at isolated signalized intersections in the city of Dhahran, Saudi Arabia [29].…”

Section: Differential Evolution (De)mentioning

confidence: 99%

Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects

Jamal¹,

Al-Ahmadi²,

Butt³

et al. 2023

Search Algorithm - Essence of Optimization

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Intelligent traffic control at signalized intersections in urban areas is vital for mitigating congestion and ensuring sustainable traffic operations. Poor traffic management at road intersections may lead to numerous issues such as increased fuel consumption, high emissions, low travel speeds, excessive delays, and vehicular stops. The methods employed for traffic signal control play a crucial role in evaluating the quality of traffic operations. Existing literature is abundant, with studies focusing on applying regression and probability-based methods for traffic light control. However, these methods have several shortcomings and can not be relied on for heterogeneous traffic conditions in complex urban networks. With rapid advances in communication and information technologies in recent years, various metaheuristics-based techniques have emerged on the horizon of signal control optimization for real-time intelligent traffic management. This study critically reviews the latest advancements in swarm intelligence and evolutionary techniques applied to traffic control and optimization in urban networks. The surveyed literature is classified according to the nature of the metaheuristic used, considered optimization objectives, and signal control parameters. The pros and cons of each method are also highlighted. The study provides current challenges, prospects, and outlook for future research based on gaps identified through a comprehensive literature review.

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Optimizing a fuzzy logic traffic signal controller via the differential evolution algorithm under different traffic scenarios

Cited by 14 publications

References 38 publications

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

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

Design of an Efficient Vehicle-Actuated Signal Control Logic for Signalized Intersections

Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects

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