Active control for traffic lights in regions and corridors: an approach based on evolutionary computation

Leal, Samara Soares; Almeida, Paulo; Chung, Edward

doi:10.1016/j.trpro.2017.05.140

Cited by 15 publications

(14 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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]. Analyzing the convergence pattern from the plots (Figure 5), it is worth to note that both the curves converge rapidly to smaller and smaller values of the objective function.…”

Section: Algorithm Procedures and Parmeters Settingmentioning

confidence: 90%

“…The fitness function evaluates each solution from offspring and corresponding parent solutions, and the best solutions are carried to the next generation. There are several selection variants DE optimization algorithm [82]. However, widely used in existing literature are: DE/Rand; which represent traditional DE version that is based on base vector random selection with uniform probability; and DE/Best; that select base vector of best individuals for the next-generation population.…”

Section: Differential Evolution (De)mentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Algorithm Procedures and Parmeters Settingmentioning

confidence: 90%

Section: Differential Evolution (De)mentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Metaheuristic approaches are one of the widely implemented by researchers in the optimization of TSC strategies. References [62][63][64][65][66][67][68][69][70][71][72][73][74][75] implemented metaheuristic algorithms such as SI, SA, GA, Bee colony, memetic algorithm, PSO, differential evolution, HS, etc. Our analysis shows that the population-based algorithms are the most widely used metaheuristic algorithms in optimizing TSC strategies.…”

Section: Metaheuristics Based Approachesmentioning

confidence: 99%

“…This means difficulty for analytical approaches and increased utilization of heuristic approaches. There are studies [7,62,65,69,71,96,97], especially under the heading of metaheuristicbased approaches (subsection 4.2), that utilize heuristics; though these applications are not enough and most of them found so far, are far away from being extensively customized. In addition, few utilize customized representations and data structures, which can be crucial in performance.…”

Section: Implications For Practicementioning

confidence: 99%

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

Qadri

Gökçe

Öner

2020

Eur. Transp. Res. Rev.

105

View full text Add to dashboard Cite

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.

show abstract

“…A review of them can be found, e.g., in [1][2][3]. Advanced techniques used recently include deep reinforcement learning [4], game theory [5], evolutionary algorithm [6], fuzzy control [7], model predictive control (MPC) [8] and others. This work proposes an MPC control strategy based on quadratic mixed-integer optimization.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control of a Road Junction

Liberati

2020

Smart Cities

View full text Add to dashboard Cite

This paper presents a model predictive control (MPC) approach for optimally managing the traffic light (TL) signals at a road junction. The objective is to improve queue balancing compared to traditional control strategies where TL signals are periodic. The resulting MPC optimization problem is of quadratic mixed-integer nature. The proposed approach is validated via simulations based on a real scenario.

show abstract

Active control for traffic lights in regions and corridors: an approach based on evolutionary computation

Cited by 15 publications

References 5 publications

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

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

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

Model Predictive Control of a Road Junction

Contact Info

Product

Resources

About