Simulation-Based Vehicular Traffic Lights Optimization

Panovski, Dancho; Zaharia, Titus

doi:10.1109/sitis.2016.49

Cited by 14 publications

(8 citation statements)

References 22 publications

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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%

“…Gökçe et al [29], Dabiri and Abbas [30], Panovski and Zaharia [31], Chuo et al [35] utilized PSO for the fulfillment of their objectives. 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.…”

Section: Metaheuristics Based Approachesmentioning

confidence: 99%

“…Results were very encouraging in terms of congestion, travel time, etc. But according to [31,75] fuzzy logic and machine-learning-based traffic controllers are not economically feasible and require a large investment for their configuration and maintenance. In terms of parameters, it has also been observed that the offset in the network of intersections has been targeted for optimization in a very limited number of studies.…”

Section: Analysis Of Findingsmentioning

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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Section: Metaheuristics Based Approachesmentioning

confidence: 99%

Section: Metaheuristics Based Approachesmentioning

confidence: 99%

Section: Analysis Of Findingsmentioning

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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“…Another paper which utilises a PSO algorithm for traffic optimisation was written by Panovski and Zaharia [75]. In this paper, the authors used SUMO for evaluating three scenarios (500 vehicles – 1600 s; 1000 vehicles – 1600 s; 2000 vehicles – 3200 s).…”

Section: Literature Analysismentioning

confidence: 99%

Recent advances in traffic optimisation: systematic literature review of modern models, methods and algorithms

Rydzewski¹,

Czarnul²

2020

IET intell. transp. syst

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Over the past few decades, the increasing number of vehicles and imperfect road traffic management have been sources of congestion in cities and reasons for deteriorating health of its inhabitants. With the help of computer simulations, transport engineers optimise and improve the capacity of city streets. However, with an enormous number of possible simulation types, it is difficult to grasp valuable, innovative solutions which are of the greatest value to city citizens. In this work, the authors expose various problems within this area having reviewed and analysed over 130 papers selected out of 1200 works in the field of urban simulations. The study describes the selection process of important papers and highlights characteristics of microsimulations, macrosimulations, computation optimisations and other approaches found in the literature which are especially useful and should be further built on in the future. They present and compare results provided in reviewed works in terms of throughput improvement, queue, waiting and travel time reduction, vehicle speed increase, speed‐ups as well as assumed simulation parameters. Finally, they focus on research gaps, such as a small number of works considering crisis simulations, few real‐world scale simulations as well as on software architectural changes and low‐level optimizations.

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“…In our work, we have retained the SUMO [42] open source platform. SUMO (Simulation of Urban MObility) is a microscopic, multi-modal traffic simulator that is able to simulate different types of traffic data.…”

Section: A Traffic Simulationmentioning

confidence: 99%