Optimization of traffic network signal timing using decentralized genetic algorithm

Tan, Min; Chuo, Helen Sin Ee; Chin, Renee Ka Yin; Yeo, Kiam Beng; Teo, Kenneth Tze Kin

doi:10.1109/i2cacis.2017.8239034

Cited by 28 publications

(12 citation statements)

References 13 publications

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“…The past decade has witnessed rapid development in information and communication technologies such as fourth generation (4G), dedicated short range communication (DSRC), Internet of Things (IOT) sensors that provide unprecedented opportunities for detailed data collection. Consequently, the fourth class of methods are based on simulations and intelligent algorithms to enhance operations at signalized intersections by optimizing cycle length, green splits and offsets [28][29][30][31]. Methods based on simulation and heuristics are more realistic, robust, and efficient in capturing the non-linear and stochastic traffic conditions [32,33].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…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%

“…To solve this problem, the discrete harmony search algorithm was employed in [63], whereas, five metaheuristics were implemented in [72]. Bie et al [64], Guo et al [71] and Tan et al [65] developed GA to optimize the TST settings of the respective networks and objective functions. Jovanović et al [66] used the BC algorithm to solve TST of isolated intersection in an undersaturated and oversaturated traffic conditions.…”

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%

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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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“…e underlying concept of most hierarchical approaches is to make network level decisions at the upper (or central) level and the real-time, small-area computations in the lower (or intersection) level. e exchange of information is a crucial aspect [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Decentralized VS Centralized Control for the Traffic Signal Synchronization Problem

Adacher

Tiriolo

2020

Journal of Advanced Transportation

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This paper suggests the adoption of a spatial decomposition method to solve the signal synchronization problem. A good signal setting maximizes the number of vehicles passing through intersections, while minimizing gas emissions and possible delays experienced by drivers. The signals synchronization issue can be defined as the problem of finding the offsets, the green timings, and the cycle length for a series of controlled intersections, minimizing the total delay of the network subject to admissibility constraints. In this paper, the authors optimized the signal setting through a new Surrogate Method calculating the objective function via the CTM UT model while performing a simulation. A spatial decomposition approach is here suggested with a simultaneous analysis of different levels of cooperation among subnetworks. This study tries to identify a subnetwork that might be representative of the entire network while taking into consideration two factors: efficiency and efficacy. A comparison between centralized and decentralized control is performed.

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Optimization of traffic network signal timing using decentralized genetic algorithm

Cited by 28 publications

References 13 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

Performance Analysis of Decentralized VS Centralized Control for the Traffic Signal Synchronization Problem

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