Dynamic traffic light timing optimization model using bat algorithm

Jintamuttha, Konlapat; Watanapa, Bunthit; Charoenkitkarn, Nipon

doi:10.1109/ccsse.2016.7784378

Cited by 8 publications

(9 citation statements)

References 7 publications

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“…The results were compared to GA. Relatively higher performance was obtained for BA algorithm as compared to GA. Jintamuttha et al carried experimental simulation for the green time of intersection for ten cycles per run [62]. The results of the experiment were optimized using BA.…”

Section: Bat Algorithm (Ba)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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Section: Bat Algorithm (Ba)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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“…Reference numbers [29][30][31][32][33][34] employ meta-heuristic methods for optimization along with a microsimulation tool. Among the meta-heuristics implementations, population-based methods were mostly employed, where PSO, ACO, and GA are the most heavily utilized methods.…”

Section: Metaheuristics Based Approachesmentioning

confidence: 99%

“…Among the meta-heuristics implementations, population-based methods were mostly employed, where PSO, ACO, and GA are the most heavily utilized methods. In addition to some common objectives that are mentioned in the previous sub(section), Elgarej et al [32,33] came up with a different objective of finding the shortest effective green time.…”

Section: Metaheuristics Based Approachesmentioning

confidence: 99%

“…Among them, [29] is the only study that has been carried out for the signalized roundabout containing 28 signal heads, whereas [30,31] worked on optimizing the arterial traffic signals having three intersections and the issues related with traffic flow management in the urban areas respectively. Jintamuttha, et al [33] proposed a finite-interval model to achieve the objective regarding TST. A different swarm-based algorithm, Bat algorithm, was utilized to relax the computational complexity.…”

Section: Metaheuristics Based Approachesmentioning

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.

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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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“…In [28] have proposed a dynamic model of optimization of traffic signal synchronization using the BA algorithm. The objective is to find an efficient and optimal time for the green light for each of the four phases of light synchronization in each cycle.…”

Section: Other Solutionsmentioning

confidence: 99%

Smart Cities: Study and Comparison of Traffic Light Optimization in Modern Urban Areas Using Artificial Intelligence

Kabrane¹,

Krit²,

Elmaimouni³

2018

IJARCSSE

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Abstract-In large cities, the increasing number of vehicles private, society, merchandise, and public transport, has led to traffic congestion. Users spend much of their time in endless traffic congestion. To solve this problem, several solutions can be envisaged. The interest is focused on the system of road signs: The use of a road infrastructure is controlled by a traffic light controller, so it is a matter of knowing how to make the best use of the controls of this system (traffic lights) so as to make traffic more fluid. The values of the commands computed by the controller are determined by an algorithm which is ultimately, only solves a mathematical model representing the problem to be solved. The objective is to make a study and then the comparison on the optimization techniques based on artificial intelligence1 to intelligently route vehicle traffic. These techniques make it possible to minimize a certain function expressing the congestion of the road network. It can be a function, the length of the queue at intersections, the average waiting time, also the total number of vehicles waiting at the intersection

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Dynamic traffic light timing optimization model using bat algorithm

Cited by 8 publications

References 7 publications

Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects

Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects

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

Smart Cities: Study and Comparison of Traffic Light Optimization in Modern Urban Areas Using Artificial Intelligence

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