Adaptive Traffic Light Control with Statistical Multiplexing Technique and Particle Swarm Optimization in Smart Cities

Manandhar, Brabim; Joshi, Basanta

doi:10.1109/cccs.2018.8586845

Cited by 8 publications

(7 citation statements)

References 5 publications

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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%

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

Qadri

Gökçe

Öner

2020

Eur. Transp. Res. Rev.

107

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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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Section: Metaheuristics Based Approachesmentioning

confidence: 99%

“…To control the flow of traffic, Manandhar and Joshi [68] developed a hybrid system that incorporated the Statistical Multiplexing technique and PSO. Based on PSO Tarek et al [67] also developed a TST control strategy for the signalized roundabout that was combined with the three different sub-controllers.…”

Section: Metaheuristics Based Approachesmentioning

confidence: 99%

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

Qadri

Gökçe

Öner

2020

Eur. Transp. Res. Rev.

107

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“…Traffic flow congestion at intersections of Kathmandu valley was analyzed using f statistical multiplexing and particle swarm optimization [ 21 ]. The proposed methodology helped in reducing the average waiting time of vehicles on the considered junctions.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent System for Intelligent Urban Traffic Management Using Wireless Sensor Networks Data

Muntean

2021

Sensors

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Intelligent traffic management is an important issue for smart cities. City councils try to implement the newest techniques and performant technologies in order to avoid traffic congestion, to optimize the use of traffic lights, to efficiently use car parking, etc. To find the best solution to this problem, Birmingham City Council decided to allow open-source predictive traffic forecasting by making the real-time datasets available. This paper proposes a multi-agent system (MAS) approach for intelligent urban traffic management in Birmingham using forecasting and classification techniques. The designed agents have the following tasks: forecast the occupancy rates for traffic flow, road junctions and car parking; classify the faults; control and monitor the entire process. The experimental results show that k-nearest neighbor forecasts with high accuracy rates for the traffic data and decision trees build the most accurate model for classifying the faults for their detection and repair in the shortest possible time. The whole learning process is coordinated by a monitoring agent in order to automate Birmingham city’s traffic management.

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“…Исследованию вопросов повышения пропускной способности путем использования информационных и коммуникационных технологий посвящен ряд работ [1][2][3][4][5][6][7][8].…”

Section: Introductionunclassified

“…Исследователи Manandhar B. и Joshi B. разработали гибридную систему управления светофорами, основанную на статическом мультиплексировании и оптимизации роя частиц [4].…”

Section: Introductionunclassified

Performance Optimization of Adaptive Traffic Lights Using Machine Vision

Shepelev¹,

Almetova²,

Moor³

et al. 2020

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Adaptive Traffic Light Control with Statistical Multiplexing Technique and Particle Swarm Optimization in Smart Cities

Cited by 8 publications

References 5 publications

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

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

Multi-Agent System for Intelligent Urban Traffic Management Using Wireless Sensor Networks Data

Performance Optimization of Adaptive Traffic Lights Using Machine Vision

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