Different Fuzzy Logic Control Strategies for Traffic Signal Timing Control with State Inputs

Tunc, Ilhan; Yesilyurt, Atakan Yasin; Söylemez, Mehmet Turan

doi:10.1016/j.ifacol.2021.06.032

Cited by 11 publications

(5 citation statements)

References 13 publications

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“…Research in this direction has been conducted worldwide in the past and present. More recent research in the field is done in [1] and [16]. Both studies reveal improved results of the fuzzy approach in comparison to traditional traffic light control strategies such as fixed traffic light control.…”

Section: Overview Of Intelligent Traffic Light Controlmentioning

confidence: 99%

“…Section 2 is calculated as the difference between the fixed duration of the full cycle of the green light and the duration of u 1 . In this case, the fixed duration of the full cycle of the green light is 100 s and the following equation holds: (1) u 1 + u 2 = 100. A triangular membership function is presented for modeling the fuzzy system with three inputs and one output.…”

Section: Fuzzy Controller Designmentioning

confidence: 99%

“…Traffic in the urban environment is mainly controlled by the cycle lengths, green durations, and phases of traffic lights [1]. These three control mechanisms can be part of an optimization problem and respectively optimization functions that optimize traffic flows, queues in front of traffic lights, delays, and other traffic indicators.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modelling and Simulation of Traffic Light Control

Vatchova,

Boneva,

Gegov

2023

Cybernetics and Information Technologies

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This study presents design of traffic light system with feedback control that considers a crossroad in an urban area. Two types of controllers are designed – fuzzy and analytical, which have been tested separately on Aimsun platform through a simulation. The aim of the study is to compare the performance of both controllers in terms of increasing traffic flow and decreasing queue length. The controllers manage the duration of the green light according to the traffic flow. Two different formal models are designed, tested, and compared. They have produced adequate solutions in terms of developing controllers for modeling and simulation of transportation tasks.

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Section: Overview Of Intelligent Traffic Light Controlmentioning

confidence: 99%

Section: Fuzzy Controller Designmentioning

confidence: 99%

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Modelling and Simulation of Traffic Light Control

Vatchova,

Boneva,

Gegov

2023

Cybernetics and Information Technologies

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“…The first category is based on traditional mathematical modeling methods, which include fuzzy logic and graph theoretic modeling algorithms [6]. Fuzzy logic algorithms employ timing control through the use of a fuzzy logic controller (FLC) to compare different proposed types of traffic light control systems based on waiting time and queue length [7]. Graph theoretic modeling algorithms transform intersection problems into a linear programming problem (LP) with the aim of minimizing waiting time by representing intersections as a network [8].…”

Section: Introductionmentioning

confidence: 99%

Traffic Signal Timing Optimization Model Based on Video Surveillance Data and Snake Optimization Algorithm

Cheng,

Qiao,

et al. 2023

Sensors

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With the continued rapid growth of urban areas, problems such as traffic congestion and environmental pollution have become increasingly common. Alleviating these problems involves addressing signal timing optimization and control, which are critical components of urban traffic management. In this paper, a VISSIM simulation-based traffic signal timing optimization model is proposed with the aim of addressing these urban traffic congestion issues. The proposed model uses the YOLO-X model to obtain road information from video surveillance data and predicts future traffic flow using the long short-term memory (LSTM) model. The model was optimized using the snake optimization (SO) algorithm. The effectiveness of the model was verified by applying this method through an empirical example, which shows that the model can provide an improved signal timing scheme compared to the fixed timing scheme, with a decrease of 23.34% in the current period. This study provides a feasible approach for the research of signal timing optimization processes.

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“…In this article, fuzzy logic control in urban traffic, a strategy to use the time of traffic lights with the state, is recommended. The traffic light system is controlled to decrease queue length and status entry [12]. This method controls the traffic light timings and phases classification to guarantee constant traffic movement with the least amount of waiting and lowest length of queue.…”

Section: Introductionmentioning

confidence: 99%

Traffic Control Prediction Design Based on Fuzzy Logic and Lyapunov Approaches to Improve the Performance of Road Intersection

2021

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Due to the increasing use of private cars for urbanization and urban transport, the travel time of urban transportation is increasing. People spend a lot of time in the streets, and the queue length of waiting increases accordingly; this has direct effects on fuel consumption too. Traffic flow forecasts and traffic light schedules were studied separately in the urban traffic system. This paper presents a new stable TS (Takagi–Sugeno) fuzzy controller for urban traffic. The state-space dynamics are utilized to formulate both the vehicle’s average waiting time at an isolated intersection and the length of queues. A fuzzy intelligent controller is designed for light control based upon the length of the queue, and eventually, the system’s stability is proved using the Lyapunov theorem. Moreover, the input variables are the length of queue and number of input or output vehicles from each lane. The simulation results describe the appearance of the proposed controller. An illustrative example is also given to show the proposed method’s effectiveness; the suggested method is more efficient than both the conventional fuzzy traffic controllers and the fixed time controller.

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Different Fuzzy Logic Control Strategies for Traffic Signal Timing Control with State Inputs

Cited by 11 publications

References 13 publications

Modelling and Simulation of Traffic Light Control

Modelling and Simulation of Traffic Light Control

Traffic Signal Timing Optimization Model Based on Video Surveillance Data and Snake Optimization Algorithm

Traffic Control Prediction Design Based on Fuzzy Logic and Lyapunov Approaches to Improve the Performance of Road Intersection

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